R. Campanini

The multiplicities of charged secondaries in proton-proton collisions were determined using the split-field-magnet detector at the CERN Intersecting Storage Rings (ISR). Measurements are presented on multiplicity distributions both for inelastic and non-single-diffractive events at four different energies √s=30.4, 44.5, 52.6, and 62.2 GeV. The results reported here represent the first high-statistics measurement of charged multiplicity distributions at ISR energies with a magnetic detector covering nearly the full solid angle.Received 23 March 1984DOI:https://doi.org/10.1103/PhysRevD.30.528©1984 American Physical Society

In this work, we present a novel approach to mass detection in digital mammograms. The great variability of the appearance of masses is the main obstacle to building a mass detection method. It is indeed demanding to characterize all the varieties of masses with a reduced set of features. Hence, in our approach we have chosen not to extract any feature, for the detection of the region of interest; in contrast, we exploit all the information available on the image. A multiresolution overcomplete wavelet representation is performed, in order to codify the image with redundancy of information. The vectors of the very-large space obtained are then provided to a first support vector machine (SVM) classifier. The detection task is considered here as a two-class pattern recognition problem: crops are classified as suspect or not, by using this SVM classifier. False candidates are eliminated with a second cascaded SVM. To further reduce the number of false positives, an ensemble of experts is applied: the final suspect regions are achieved by using a voting strategy. The sensitivity of the presented system is nearly 80% with a false-positive rate of 1.1 marks per image, estimated on images coming from the USF DDSM database.

We have measured the differential cross section for pp and p̄p elastic scattering at √s = 31, 53 and 62 GeV in the interval 0.05 < |t| < 0.85 GeV2 at the CERN ISR using the Split Field Magnet detector. At 53 and 62 GeV, for 0.17 < |t| < 0.85 GeV2 both pp and p̄p data show simple exponential behaviour in t; at √s = 31 GeV the data for 0.05 < |t| < 0.85 GeV2 are consistent with a change in slope near |t| = 0.15 GeV2.

In a sample of diffractive events of high multiplicity a sharp five standard deviation signal is observed at M = 2255 MeV/c2 in the K−pπ+ mass distribution and, although with less statistical strength, at the same mass in the Λ0π+π+π− channel. These signals are identified as being due to the decay of the charmed baryon Λ+c which is produced with a cross section times branching ratio σcB in the range 0.7−1.8 μb for the K−π+p decay and 0.3−0.7 μb for the Λ-π+π+ π− system.

In this paper we investigate the feasibility of using an SVM (support vector machine) classifier in our automatic system for the detection of clustered microcalcifications in digital mammograms. SVM is a technique for pattern recognition which relies on the statistical learning theory. It minimizes a function of two terms: the number of misclassified vectors of the training set and a term regarding the generalization classifier capability. We compare the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of our detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features. The SVM classifier gets slightly better results than the MLP one (Az value of 0.963 against 0.958) in the presence of a high number of training data; the improvement becomes much more evident (Az value of 0.952 against 0.918) in training sets of reduced size. Finally, the setting of the SVM classifier is much easier than the MLP one.

States of volcanic activity at Mt Etna develop in well-defined regimes with variable duration from a few hours to several months. Changes in the regimes are usually concurrent with variations of the characteristics of volcanic tremor, which is continuously recorded as background seismic radiation. This strict relationship is useful for monitoring volcanic activity in any moment and in whatever condition. We investigated the development of tremor features and its relation to regimes of volcanic activity applying pattern classification techniques. We present results from supervised and unsupervised classification methods applied to 425 patterns of volcanic tremor recorded between 2001 July and August, when a volcano unrest occurred.

Purpose: The authors presented a novel system for automated nodule detection in lung CT exams. Methods: The approach is based on (1) a lung tissue segmentation preprocessing step, composed of histogram thresholding, seeded region growing, and mathematical morphology; (2) a filtering step, whose aim is the preliminary detection of candidate nodules (via 3D fast radial filtering) and estimation of their geometrical features (via scale space analysis); and (3) a false positive reduction (FPR) step, comprising a heuristic FPR, which applies thresholds based on geometrical features, and a supervised FPR, which is based on support vector machines classification, which in turn, is enhanced by a feature extraction algorithm based on maximum intensity projection processing and Zernike moments. Results: The system was validated on 154 chest axial CT exams provided by the lung image database consortium public database. The authors obtained correct detection of 71% of nodules marked by all radiologists, with a false positive rate of 6.5 false positives per patient (FP/patient). A higher specificity of 2.5 FP/patient was reached with a sensitivity of 60%. An independent test on the ANODE09 competition database obtained an overall score of 0.310. Conclusions: The system shows a novel approach to the problem of lung nodule detection in CT scans: It relies on filtering techniques, image transforms, and descriptors rather than region growing and nodule segmentation, and the results are comparable to those of other recent systems in literature and show little dependency on the different types of nodules, which is a good sign of robustness.

The purpose of this study was to perform a complete evaluation of three pieces of clinical digital mammography equipment. Image quality was assessed by performing physical characterization and contrast‐detail (CD) analysis. We considered three different FFDM systems: a computed radiography unit (Fuji “FCR 5000 MA”) and two flat‐panel units, the indirect conversion a‐Si based GE “Senographe 2000D” and the direct conversion a‐Se based IMS “Giotto Image MD.” The physical characterization was estimated by measuring the MTF, NNPS, and DQE of the detectors with no antiscatter grid and over the clinical range of exposures. The CD analysis was performed using a CDMAM 3.4 phantom and custom software designed for automatic computation of the contrast‐detail curves. The physical characterization of the three digital systems confirms the excellent MTF properties of the direct conversion flat‐panel detector (FPD). We performed a relative standard deviation (RSD) analysis, for investigating the different components of the noise presented by the three systems. It turned out that the two FPDs show a significant additive component, whereas for the CR system the statistical noise is dominant. The multiplicative factor is a minor constituent for all the systems. The two FPDs demonstrate better DQE, with respect to the CR system, for exposures higher than . The CD analysis indicated that the three systems are not statistically different for detail objects with a diameter greater than . However, the IMS system showed a statistically significant different response for details smaller than . In this case, the poor response of the a‐Se detector could be attributed to its high‐frequency noise characteristics, since its MTF, NEQ, and DQE are not inferior to those of the other systems. The CD results were independent of exposure level, within the investigated clinical range. We observed slight variations in the CD results, due to the changes in the visualization parameters (window/level and magnification factor). This suggests that radiologists would benefit from viewing images using varied window/level and magnification.

Abstract IGHV3-21–using chronic lymphocytic leukemia (CLL) is a distinct entity with restricted immunoglobulin gene features and poor prognosis and is more frequently encountered in Northern than Southern Europe. To further investigate this subset and its geographic distribution in the context of a country (Italy) with both continental and Mediterranean areas, 37 IGHV3-21 CLLs were collected out of 1076 cases enrolled by different institutions from Northern or Central Southern Italy. Of the 37 cases, 18 were identified as homologous (hom)HCDR3–IGHV3-21 CLLs and were found almost exclusively (16 of 18) in Northern Italy; in contrast, 19 nonhomHCDR3–IGHV3-21 cases were evenly distributed throughout Italy. Clinically, poor survivals were documented for IGHV3-21 CLLs as well as for subgroups of mutated and homHCDR3–IGHV3-21 CLLs. Negative prognosticators CD38, ZAP-70, CD49d, and CD79b were expressed at higher levels in homHCDR3 than nonhomHCDR3–IGHV3-21 cases. Differential gene expression profiling (GEP) of 13 IGHV3-21 versus 52 non–IGHV3-21 CLLs identified, among 122 best-correlated genes, TGFB2 and VIPR1 as down- and up-regulated in IGHV3-21 CLL cases, respectively. Moreover, GEP of 7 homHCDR3 versus 6 nonhomHCDR3–IGHV3-21 CLLs yielded 20 differentially expressed genes, with WNT-16 being that expressed at the highest levels in homHCDR3–IGHV3-21 CLLs. Altogether, IGHV3-21 CLLs, including those with homHCDR3, had a peculiar global phenotype in part explaining their worse clinical outcome.

We applied an automatic pattern recognition technique, known as Support Vector Machine (SVM), to classify volcanic tremor data recorded during different states of activity at Etna volcano, Italy. The seismic signal was recorded at a station deployed 6 km southeast of the summit craters from 1 July to 15 August, 2001, a time span encompassing episodes of lava fountains and a 23 day‐long effusive activity. Trained by a supervised learning algorithm, the classifier learned to recognize patterns belonging to four classes, i.e., pre‐eruptive, lava fountains, eruptive, and post‐eruptive. Training and test of the classifier were carried out using 425 spectrogram‐based feature vectors. Following cross‐validation with a random subsampling strategy, SVM correctly classified 94.7 ± 2.4% of the data. The performance was confirmed by a leave‐one‐out strategy, with 401 matches out of 425 patterns. Misclassifications highlighted intrinsic fuzziness of class memberships of the signals, particularly during transitional phases.

We observe an increase of the average transverse momentum with the multiplicity of the charged particles produced in the central rapidity region of proton-proton collisions at ISR energies. The increase is smaller than that observed at the CERN proton-antiproton collider. The magnitude of the effect is the same for negative and positive particles.

A novel invariant texture classification method is proposed. Invariance to linear/non-linear monotonic gray-scale transformations is achieved by submitting the image under study to the ranklet transform, an image processing technique relying on the analysis of the relative rank of pixels rather than on their gray-scale value. Some texture features are then extracted from the ranklet images resulting from the application at different resolutions and orientations of the ranklet transform to the image. Invariance to 90°-rotations is achieved by averaging, for each resolution, correspondent vertical, horizontal, and diagonal texture features. Finally, a texture class membership is assigned to the texture feature vector by using a support vector machine (SVM) classifier. Compared to three recent methods found in literature and having being evaluated on the same Brodatz and Vistex datasets, the proposed method performs better. Also, invariance to linear/non-linear monotonic gray-scale transformations and 90°-rotations are evidenced by training the SVM classifier on texture feature vectors formed from the original images, then testing it on texture feature vectors formed from contrast-enhanced, gamma-corrected, histogram-equalized, and 90°-rotated images.

We deduce approximate equations of state from experimental measurements in proton-proton and proton-antiproton collisions. Thermodynamic quantities are estimated combining the measure of average transverse momentum <pt> vs pseudorapidity density dN/deta with the estimation of the interaction region size from measures of Bose Einstein correlation, or from a theoretical model which relates dN/deta to the impact parameter. The results are very similar to theory predictions in case of crossover from hadron gas to quark gluon plasma. According to our analysis, the possible crossover should start at dN/deta about 6 and end at dN/deta about 24.

The average transverse momentum and the size of the particle emitting source (measured via Bose-Einstein correlations) have been studied as functions of the charged particle density in the central region inp-p interactions at $$\sqrt s $$ =62, 44 and 31 GeV. Both the average transverse momentum and the source size increase with increasing density at all three energies. This effect, very weak at $$\sqrt s $$ =31 GeV, becomes stronger with increasing energy.

The ratios of high pT charged kaon to pion production cross sections at √s = 45 and 62 GeV are presented. The values of the K±π± ratios are essentially independent of both √s and xT = 2pT√s and are compatible with a strangeness suppression factor λ = 0.55. By contrast, the K−π− values fall with xT suggesting a gluonic origin of K−. QCD calculations agrees with the measurements.

Data are presented on Pomeron-Pomeron interactions which produce a centralπ + π − system in proton-proton collisions at $$\sqrt s = 62 GeV$$ at the CERN Intersecting Storage Rings. This process may favor the production of gluonic bound states. A partial-wave analysis of theπ + π − system shows evidence for the production of the statesf 0(975),f 0(1400), andf 2(1270). The fitted mass for thef 2(1270) is about 50 MeV below the world average. In addition, the production mechanism for thef 2(1270) is uniquely different from that for the other final states in that there is a correlation between the outgoing protons. this is consistent with a picture of two-gluon exchange with thef 2(1270) produced by gluon fusion, and could indicate that thef 2(1270) has a glueball component.

Abstract With the aim of identifying the immunophenotypic profile of B‐cell chronic lymphocytic leukemia (B‐CLL) subsets with different prognosis, we investigated by flow cytometry the expression of 36 surface antigens in 123 cases, all with survivals. By analyzing results with unsupervised (hierarchical and K‐means clustering) algorithms, three distinct immunophenotypic groups (I, II, and III) were identified, group I (51/123) with longer survivals, as compared to the group II (36/123) and III (36/123). The immunophenotypic signatures of these groups, as determined by applying the nearest Shrunken centroids method as class predictor, were characterized by the coordinated and differential expression of 12 surface markers, that is, group I: above‐average expression of CD62L, CD54, CD49c, and CD25, below‐average expression of CD38; group II: above‐average expression of CD38, CD49d, CD29, and CD49e; and group III: below‐average expression of the above markers, overexpression of CD23, CD20, SmIg, and CD79b. As opposed to groups II–III, group I B‐CLLs lacked expression of ZAP‐70 and activation‐induced cytidine deaminase in the majority of cases, while more frequently had mutated IgV H genes and IgV H mutations consistent with antigen‐driven selection. Our findings contribute to improve the immunophenotypical identification of disease subsets with different prognosis and suggest a set of surface antigens to be employed as prognosticators in routine diagnostic/prognostic procedures. © 2004 Wiley‐Liss, Inc.

Abstract Measurements are presented of inclusive distributions in pp and p p interactions at √s = 53 GeV. The data were obtained at the CERN ISR using the Split Field Magnet spectrometer with a minimum bias trigger. The inclusive distributions are analyzed as functions of the transverse momentum, the rapidity and the Feynman-x variable.

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with their gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks when compared to the previous one. Specifically, at 60%, 65%, and 70% per-mammogram sensitivity, the new CAD system achieves 0.50, 0.68, and 0.92 FP marks per mammogram, whereas at 70%, 75%, and 80% per-case sensitivity it achieves 0.37, 0.48, and 0.71 FP marks per mammogram, respectively. Conversely, at the same sensitivities, the previous CAD system reached 0.71, 0.87, and 1.15 FP marks per mammogram, and 0.57, 0.73, and 0.92 FPs per mammogram. Also, statistical significance of the difference between the two per-mammogram and per-case FROC curves is demonstrated by the p-value < 0.001 returned by jackknife FROC analysis performed on the two CAD systems.

Data are presented for the exclusive reaction pp → pp π+ π− at $$\sqrt s = 62GeV$$ with two leading protons at large Feynman-x and a centrally produced π+;π− system. In this kinematical configuration one expects a substantial contribution from Double Pomeron Exchange, which is a potential source of glueballs. The experiment was performed at the CERN ISR using the Split Field Magnet spectrometer. In the mass range between 1,000 and 1,700 MeV/c2 the invariant mass distribution for the central π+;π− system exhibits a very significant signal for thef 0(1270) and no other obvious resonant states.

We analyse the variation of the average transverse momentum, 〈pT〉, with the multiplicity of charged particles produced in pp, p̄p, dd and αα collisions at ISR energies. An increase of 〈pT〉 with increasing particle density p=ΔnΔy for charged particles produced in the central region is observed. The energy dependence of this effect and its dependence on the type of colliding particles are discussed.

The multiplicity distributions of charged secondaries in proton-proton interactions at $$\sqrt s = 31$$ , 44 and 62 GeV have been measured with high statistics using the split field magnet (SFM) detector at the CERN ISR. The multiplicity distributions for narrow bins in rapidity depend on the rapidity variable. The validity of KNO scaling is discussed. The negative binomial function fits well the charged and negative multiplicity data for full phase space and for central rapidity windows. The energy and the rapidity dependence of the negative binomial parameters, $$\bar n$$ andk, are presented. We also analyse their implications for a cascade model in terms of the average number of «clans» and of the average number of particles per «clan».

The classification of tumoral masses and normal breast tissue is targeted. A mass detection algorithm which does not refer explicitly to shape, border, size, contrast or texture of mammographic suspicious regions is evaluated. In the present approach, classification features are embodied by the image representation used to encode suspicious regions. Classification is performed by means of a support vector machine (SVM) classifier. To investigate whether improvements can be achieved with respect to a previously proposed overcomplete wavelet image representation, a pixel and a discrete wavelet image representations are developed and tested. Evaluation is performed by extracting 6000 suspicious regions from the digital database for screening mammography (DDSM) collected by the University of South Florida (USF). More specifically, 1000 regions representing biopsy-proven tumoral masses (either benign or malignant) and 5000 regions representing normal breast tissue are extracted. Results demonstrate very high performance levels. The area A z under the receiver operating characteristic (ROC) curve reaches values of 0.973 ± 0.002, 0.948 ± 0.004 and 0.956 ± 0.003 for the pixel, discrete wavelet and overcomplete wavelet image representations, respectively. In particular, the improvement in the A z value with the pixel image representation is statistically significant compared to that obtained with the discrete wavelet and overcomplete wavelet image representations (two-tailed p-value &lt; 0.0001). Additionally, 90% true positive fraction (TPF) values are achieved with false positive fraction (FPF) values of 6%, 11% and 7%, respectively.

In $\mathrm{pp}$ collisions at $\sqrt{s}=62.3$ GeV where each proton fragments into at least one low-${p}_{T}$, high-$x$ pion, no significant correlations for ${\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{+}$, ${\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}$, and ${\ensuremath{\pi}}^{\ensuremath{-}}{\ensuremath{\pi}}^{\ensuremath{-}}$ are found, thus excluding quark exchange with Brodsky-Gunion counting rules as the dominant interaction mechanism. This, together with the experimental $x$ dependence of single-particle spectra, suggests the introduction of a new counting rule, which is discussed.

Data are presented for the first time on inclusive Pomeron-Pomeron interactions which produce a central systemX (composed mainly of multimeson states) in proton-proton collisions at $$\sqrt s $$ at the CERN ISR. The systemX has a Feynman-x distribution which is sharply peaked atx f=0, is inconsistent with any significant contributions from Reggeon exchange processes, and has an invariant mass dependence in good agreement with the predicted formM −2 . Kaon production is about 15% of pion production, nearly independent ofM x, while proton-antiproton production averages about 5% of pion production and increases withM x. The structure of the central systemX develops into a jetlike shape, asM x increases, as would be expected from a model of Pomeron fragmentation. The shape of thex f(π) distribution in the center of mass of theX system (although not proving existence) is consistent with asoft partonic substructure of the Pomeron.

Studies of gene expression profiling (GEP) have been successfully used for the identification of molecules to be employed as potential prognosticators. With the aim of identifying the immunophenotypic profile of B-CLL subsets with different prognoses, we investigated by flow cytometry the expression of 36 surface antigens in 117 cases, 113 with survival data. In analogy with GEP, results were analyzed by applying unsupervised hierarchical algorithms (surface-antigen expression profiling, SEP). Distinct immunophenotypic groups (A, B1, B2 and C) were identified, group C (57/117) with longer survivals, as compared to groups A (23/117), B1 (16/117) and B2 (21/117). The immunophenotypic signatures of these groups were characterized by the coordinated and differential over-expression of: i) CD62L, CD54 and CD49c (group C); ii) CD38 and CD49d (group A); iii) none of the above markers (group B1 and B2). Other molecules were either not expressed, widely expressed by all samples, or were variably expressed within the observed B-CLL subgroups, although without a clearly distinguishable pattern. By employing an identical approach for investigating the reactivity of B-cell panel monoclonal antibodies (B-mAbs) in B-CLLs (29 cases) and in 19 B and non-B leukemia/lymphoma cell lines, we found mAbs (B012, B001, B006, B018, B019, B020, B017) mainly unreactive in all the samples, mAbs (B002, B010, B013, B014, B015) strongly reactive in B-CLLs and B-cell lines but not in non-B-cell lines, and mAbs recognizing antigens variably expressed in cell lines and B-CLLs. A hierarchical clustering focused on B-CLLs alone, combining reactivity values for B-mAbs with the expression of CD62L and CD38, these latter antigens identified as leader markers of B-CLL subsets with different prognosis, demonstrated a correlation between CD62L expression and the reactivity of B007, B003, B011 and B005 mAbs. These mAbs may represent potentially novel markers with prognostic relevance in B-CLLs.

A new gamma camera dedicated to scintimammography (single photon emission mammography-SPEM) now has a full-breast field of view. One can clinically examine a mildly compressed breast with a cranio-caudal-like projection as one would in X-ray mammography. This camera is based on pixelated scintillation arrays and position sensitive photomultiplier tubes. By reducing the collimator-tumor distance, we enhanced the geometric spatial resolution and the contrast. Unfortunately, due to the low counting rates in scintimammography, low contrast images are usually seen, particularly with small tumors. The aim of this paper is to evaluate how a camera, based on a pixelated detector, can improve the SNR values for small tumors by effectively correcting the spatial response. The procedure is based on good pixel identification. We used a small gamma camera with a metal channel dynode position sensitive photomultiplier (Hamamatsu R7600-C8) coupled to different CsI(Tl) scintillator arrays with a general purpose collimator. This type of photomultiplier drastically reduces the charge spread and improves the intrinsic characteristics of the imager. The dimensions of the CsI (Tl) matched the photomultiplier's active area (22 /spl times/ 22 mm/sup 2/). Utilizing its very high intrinsic spatial resolution, we created a look up table to correct gain and spatial nonuniformities. We used a breast and torso phantom to characterize the SNR as a function of pixel size, thickness of the breast, tumor size, and depth. The data showed that the SNR depends principally on the match between the tumor and pixel size. For instance, for a 6 mm diameter tumor, the best SNRs were obtained by a 2 /spl times/ 2 mm/sup 2/ array. For larger tumors, up to 10 mm diameter, a larger pixel 3 /spl times/ 3 mm/sup 2/ or 4 /spl times/ 4 mm/sup 2/, optimizes the SNR value. We compared the results of this camera with those from both a SPEM gamma camera and a standard Anger camera.

Measurements are presented of two-particle rapidity correlations in pp and pp at √s = 53 GeV. The data were recorded at the CERN-ISR using the Split Field Magnet spectrometer with a minimum bias trigger. Short range correlations in normal inelastic events with measured charged multiplicities nch ⩾ 4 are observed of pairs for charged particles in all charge combinations. Within the experimental errors no differences are observed between the analogous correlations in pp and pp interactions.

In this work we describe a parallel system consisting of feed-forward neural networks supervised by a local genetic algorithm. The system is implemented in a transputer architecture and is used to predict the secondary structures of globular proteins. This method allows a wide search in the parameter space of the neural networks and the determination of their optimal topology for the predictive task. Different neural network topologies are selected by the genetic algorithm on the basis of minimal values of mean square errors on the testing set. When the α-helix, β-strand and random coil motifs of secondary structures are discriminated, the maximal efficiency obtained is 0.62, with correlation coefficients of 0.35, 0.31 and 0.37 respectively. This level of accuracy is similar to that previously attained by means of neural networks without hidden layers and using single protein sequences as input. The results validate the neural network topologies used for the prediction of protein secondary structures and highlight the relevance of the input information in determining the limit of their performance.

Data are reported on the momentum distributions of Λ, Λ (1520), φ (1020), Λ and p, inclusively produced between 1° and 2° with respect to one of the primary proton beams at the CERN Intersecting Storage Rings. In addition, the decay angular distribution of the Λ(1520), the ratio of the cross sections for the production of Σ− (1385) and Σ+ (1385) and the ratios among different charge states of the pairs Λπ, ΛK, Δ++ (1232)π and ππ have been measured. These data are confronted with current ideas on fragmentation.

The capability of the scintimammography to diagnose subcentimeters sized tumors was increased by the employment of a dedicated gamma camera. The introduction of small field of view camera, based on pixellated scintillation array and position sensitive photomultiplier, allowed to enhance the geometric spatial resolution and contrast of the images due to reduced collimator-tumor distance. The aim of this paper is to investigate the realistic possibility of T1a tumors detection (/spl sim/5 mm size) by comparing the signal-to-noise ratio (SNR) values obtained by different imagers. To this end, we have utilized a self-designed solid breast phantom with different sized hot spots (tumors). The phantom consists of seven disks with different thickness, molded from resin epoxy activated with Co/sup 57/ isotope. The overlapped disks represent a pendula breast with about 800 cc volume. Hot spots have not wall. One disk has holes to fit the hot spots representing the different sized lesions. The imagers utilized were: a standard Anger Camera and three different cameras based on scintillator array, CsI(Tl) or NaI(Tl), coupled to position sensitive photomultiplier with different technologies, to make detectors with field of view of 3 and 5 inch. The experimental results are supported by Monte Carlo simulation. It was highlighted how spatial resolution is a predominant element in tumor visibility and how background causes a reduction of the image contrast. All gamma cameras show close results at SNR values less than 10 and a full detectability of 8 mm tumor size. However, the results show the 5 mm tumor size is lower detection limit for all cameras.

In this paper, we investigate the improvement obtained by applying a distributed genetic algorithm to a problem of parameter optimization in medical images analysis. We setup a method for the detection of clustered microcalcifications in digital mammograms, based on statistical techniques and multiresolution analysis by means of wavelet transform. The optimization of this scheme requires multiple runs on a set of 40 images, in order to obtain relevant statistics.We aim to evaluate how fluctuations of some parameters values of the detection method influence the performance of our system. A distributed genetic algorithm supervising this process allowed to improve of some percents previous results obtained after having “hand tuned” these parameters for a long time. At last, we have been able to find out parameters not influencing performance at all.

Forward particle production in $\mathrm{pp}$ interactions triggered by a 30\ifmmode^\circ\else\textdegree\fi{} pion of momentum exceeding 5 GeV/c has been studied at c.m. energy of 63 GeV. Quantum-chromodynamic model calculations show that in a majority of such interactions the incident proton loses a valence quark. Results indicate that hadronization of diquarks is primarily a recombination process leading to the formation of a high-$x$ baryon.

Abstract Studies of gene expression profiling have been successfully used for the identification of molecules to be employed as potential prognosticators. In analogy with gene expression profiling, we have recently proposed a novel method to identify the immunophenotypic signature of B-cell chronic lymphocytic leukemia subsets with different prognosis, named surface-antigen expression profiling. According to this approach, surface marker expression data can be analysed by data mining tools identical to those employed in gene expression profiling studies, including unsupervised and supervised algorithms, with the aim of identifying the immunophenotypic signature of B-cell chronic lymphocytic leukemia subsets with different prognosis. Here we provide an overview of the overall strategy employed for the development of such an "outcome class-predictor" based on surface-antigen expression signatures. In addition, we will also discuss how to transfer the obtained information into the routine clinical practice by providing a flow-chart indicating how to select the most relevant antigens and build-up a prognostic scoring system by weighing each antigen according to its predictive power. Although referred to B-cell chronic lymphocytic leukemia, the methodology discussed here can be also useful in the study of diseases other than B-cell chronic lymphocytic leukemia, when the purpose is to identify novel prognostic determinants.

A prototype for Positron Emission Mammography, the YAP-PEM, is under development within a collaboration of the Italian Universities of Pisa, Ferrara, and Bologna. The aim is to detect breast lesions, with dimensions of 5 mm in diameter, and with a specific activity ratio of 10:1 between the cancer and breast tissue. The YAP-PEM is composed of two stationary detection heads of 6×6 cm2, composed of a matrix of 30×30 YAP:Ce finger crystals of 2×2×30 mm3 each. The EGSnrc Monte Carlo code has been used to simulate several characteristics of the prototype. A fast EM algorithm has been adapted to reconstruct all of the collected lines of flight, also at large incidence angles, by achieving 3D positioning capability of the lesion in the FOV. The role of the breast compression has been studied. The performed study shows that a 5 mm diameter tumor of 37 kBq/cm3 (1 μCi/cm3), embedded in active breast tissue with 10:1 tumor/background specific activity ratio, is detected in 10 min with a Signal-to-Noise Ratio of 8.7±1.0. Two hot lesions in the active breast phantom are clearly visible in the reconstructed image.

We have developed a tomograph for single photon emission imaging (SPECT) of the breast for the detection of small size tumors. The SPECT is mounted on a ring that is rotating around the breast with the patient in prone position. The breast will be imaged by two opposing detector heads of approximately 5×15cm2 each, with a field of view about 13 cm wide. Each head is made up of one pixilated NaI crystal matrix coupled to three Hamamatsu H8500 PMTs. A “general purpose” lead collimator is positioned in front of the crystal. Detailed simulations have been made for the optimization and the evaluation of the detector performances. Monte Carlo results indicate that tumors of 8 mm diameter are detectable with a tumor/background uptake ratio of 5:1. The experimental characterization of the detector head is presented. The rotating ring is now being assembled.

We describe a stand‐alone software utility named TREMOrEC, which carries out training and test of a Support Vector Machine (SVM) classifier. TREMOrEC is developed in Visual C++ and runs under Microsoft Windows operating systems. Ease of use and short time processing, along with the excellent performance of the SVM classifier, make this tool ideal for volcano monitoring. The development of TREMOrEC is motivated by the successful application of the SVM classifier to volcanic tremor data recorded at Mount Etna in 2001. In that application, spectrograms of volcanic tremor were divided according to their recording date into four classes associated with different states of activity, i.e., pre‐eruptive, lava fountain, eruptive, or post‐eruptive. During the training, SVM learned the a priori classification. The classifier's performance was then evaluated on test sets not considered for training. The classification results matched the actual class membership with an error of less than 6%.

Events are analyzed in which a high transverse momentum proton was produced at polar angles of 10°, 20° and 45°. The experiment was performed with the Split Field Magnet detector at the CERN ISR at $$\sqrt s $$ =62 GeV. A 4-jet structure of these events is found [1]. The measured charge structure of spectator jets is compatible with proton production from hard diquark scattering. This is supported by a study of baryon number compensation in the towards jets. The observed charge compensation in the towards jets suggests dominance of hard (ud) scattering. Evidence forΔ ++ production at high transverse momentum indicates the presence of an additional (uu) scattering component. The properties of the recoiling away jets are compatible with the fragmentation of a valence quark and/or of a gluon as in the case of meson triggers.

In a high-statistics experiment at the CERN-ISR we analyze the variation of the average transverse momentum in fixed rapidity intervals as a function of the multiplicity density of charged particles produced in proton-proton collisions at √s = 31, 44 and 62 GeV. The average transverse momentum depends on both rapidity y and charged multiplicity density, ρ = Δn/Δy. The dependence on ρ changes gradually from an increase at low y, constancy at intermediate y and decrease at large y.

High energy proton-proton interactions yielding a single trigger particle with large transverse momentum give rise to a four-jet event structure with two transverse jets and two jets along the beam direction. The transverse jets are due to the fragmentation of point like scattered partons. It is shown that the quantum numbers of triggering charged pions and positive kaons are correlated with the flavour of the scattered parent parton; thus one can enhance data samples with a particular flavour of a scattered parton. The analysis, which is independent of detailed model calculations, exploits (a) the identification of the leading particles in the trigger jets (trigger particles), (b) the measurement of their relative production rates, (c) short range quantum number correlations within the trigger jets, and (d) long range correlations between leading particles from different jets. The data were obtained at $$\sqrt s $$ =62 GeV with the Split Field Magnet detector at the CERN ISR.

Production of charmed particles has been investigated at √s = 63 GeV usi ng the Lampshade Magnet detector triggered by electrons and positrons emitted at 30% from the ISR beam axis. The results of a search for Λc and Λc signals in the K−pπ+ and K+pπ− channels are presented. Cross sections for the reactions pp → DΛcX and pp → ΛcΛcX, and upper limits. for pp → DDX, are evaluated under various models and compared with other values obtained at the ISR.

Measurements are presented of short range three-particle rapidity correlation in pp interactions at c.m. energies [Formula: see text], 44 and 62 GeV. The data were obtained at the CERN Intersecting Storage Rings (ISR) using the Split Field Magnet Detector (SFM) with a minimum bias trigger. Three-particle short range rapidity correlations are observed for the (-+-) and (+-+) combinations; no short-range correlation is observed for the (---) and (+++) configurations. The rapidity range of the three-particle correlations is approximately the same as for the two-particle correlations.

Inclusive cross sections forΔ ++ production inpp interactions at different ISR energies are presented. The differential cross sectiondσ/dx forΔ ++ production is found to be approximately independent of Feynmanx. No strong energy dependence is seen over the ISR energy range. The topological cross sections ofΔ ++ at $$\sqrt s = 62$$ GeV show an appreciable contribution from non-diffractive production mechanisms. An upper limit for theΔ 0 production cross section is determined.

In this paper we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists on the combination of two different methods. The first one, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second one is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. In the falsepositive reduction step we separate false signals from microcalcifications by means of an SVM classifier. Our algorithm yields a sensitivity of 94.6% with 0.6 false positive cluster per image on the 40 images of the Nijmegen database.

Data are presented on pomeron-pomeron interactions which produce a central π+π−π+π− system in proton-proton collisions at √s=62 GeV at the CERN Intersecting Storage Rings. A spin-parity analysis of the π+π−π+π− system shows evidence for the production of the statef 2 (1720) with decay to ρ0π+π−. Since pomeron-pomeron interactions are expected to favor the production of gluonic bound states, observation of thef 2 (1720) supports earlier interpretations of it as a glueball. In addition, enhancements near threshold give indication of the statef 2 (1270) decaying to ρ0ρ0 and the statef 0 (1400) decaying to ρ0π+π−.

In this paper, we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists of the combination of two different methods. The first, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second, is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. We can separately tune the two methods, so that each one of them is able to detect signals with similar features. By combining signals coming out from the two parts through a logical OR operation, we can discover microcalcifications with different characteristics. Our algorithm yields a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.

We have developed a method for the detection of clusters of microcalcifications in digital mammograms. Here, we present a genetic algorithm used to optimize the choice of the parameters in the detection scheme. The optimization has allowed the improvement of the performance, the detailed study of the influence of the various parameters on the performance and an accurate investigation of the behavior of the detection method on unknown cases. We reach a sensitivity of 96.2% with 0.7 false positive clusters per image on the Nijmegen database; we are also able to identify the most significant parameters. In addition, we have examined the feasibility of a distributed genetic algorithm implemented on a non-dedicated Cluster Of Workstations. We get very good results both in terms of quality and efficiency.

We report on measurements of charged pion production cross sections at θ ≅ 50°, pT ≅ 3–9 GeV/c and √s = 45 GeV, taken with the Split Field Magnet Detector at the CERN Intersecting Storage Rings (ISR). Together with previous data at √s = 62 GeV, this allows the calculation of the exponent n assuming a power law dependence pnT. Values of n ≈ 8 are found at low xT = 2pT/√s which drop to about 7 at xT ≈ 0.3. The measured values of π+/π− rise with xT and approach ≈ 2 at xT ≈ 0.3. A first-order QCD calculations is reasonably consistent with the data.

This study investigates the possibility of using an Artificial Neural Network (ANN) for reconstructing Positron Emission Tomography (PET) images. The network is trained with simulated data which include physical effects such as attenuation and scattering. Once the training ends, the weights of the network are held constant. The network is able to reconstruct every type of source distribution contained inside the area mapped during the learning. The reconstruction of a simulated brain phantom in a noiseless case shows an improvement if compared with Filtered Back-Projection reconstruction (FBP). In noisy cases there is still an improvement, even if we do not compensate for noise fluctuations. These results show that it is possible to reconstruct PET images using ANNs. Initially we used a Dec Alpha; then, due to the high data parallelism of this reconstruction problem, we ported the learning on a Quadrics (SIMD) machine, suited for the realization of a small medical dedicated system. These results encourage us to continue in further studies that will make possible reconstruction of images of bigger dimension than those used in the present work (32 × 32 pixels).

The contribution of single diffraction dissociation to highp T particle production has been determined with the split field magnet detector. For transverse momentap T >2 GeV/c and production angles θ>10° it is found to be less than 5% for all types of trigger particles. Specifically, a diffractive origin of the large production cross section for highp T protons can be excluded. The contributions found to highp T production are found to be in good agreement with extrapolations of the relative particle yields from single diffraction dissociation observed in inelastic minimum bias events.

EGS is a very common Monte Carlo code, used in the simulation of Nuclear Medicine devices. Simulation techniques are particularly useful, in order to optimize collimator configuration and camera design in Single Photon Emission studies. Using the EGS code, users must define the geometry where particles are transported. This can be both a very hard task and a source of inefficiency, especially in case of complex geometries as, for instance, hexagonal hole collimators or pixellated detectors. In this paper we present a modular description for such geometries. Regions are seen as a basic cell repeated in the space. Our method allows the computation of the region to which a point belongs to in a few steps; thus we are able to calculate this region in a reduced number of operations, independently from the collimator and detector dimensions. We validated the modular description, by performing the characterization of two different collimators: one with square holes and one with hexagonal holes. With modular description we can reduce the computational time up to 25%, with respect to a "traditional" geometric description. It is also possible to simulate a breast phantom for different configurations: each run (one phantom relative to a 10 min acquisition with full photons and electrons transport) would take almost 24 hours on a cluster of four PIII 800 MHz processors.

A prototype for positron emission mammography is under development within a collaboration of the Italian Universities of Pisa, Ferrara, Bologna and Roma. The device is composed of two stationary detection heads, each with an active area of 6 cm /spl times/ 6 cm, made of 30/spl times/30 YAP:Ce finger crystals of 2 mm /spl times/ 2 mm /spl times/ 30 mm. The EGSnrc Monte Carlo code has been used to perform a complete simulation of this camera. We have used a fast three-dimensional iterative algorithm (30 s per iteration on a PC-Pentium III 800 MHz processor) for image reconstruction. The performed study indicates that tumors of 5 mm diameter, i.e., 0.065 cm/sup 3/ volume, with 37 kBq/cm/sup 3/ (1 /spl mu/Ci/cm/sup 3/) specific activity embedded in a breast active phantom, are detectable in 10 minutes for a 10:1 tumor/background ratio with an 8.7 Signal-to-Noise Ratio value. Experimental measurements with the small animal tomograph YAP-PET have validated the Monte Carlo predictions.

The production of ρ(770)0,K *(892)0, $$\bar K^* (892)^0 $$ andf 2(1270) mesons as leading particles in hadron jets (<z>∼0.7) has been measured in deep inelasticpp interactions at the CERN ISR at $$\sqrt s = 62$$ GeV. The comparison of vector and pseudoscalar meson production at the same transverse momentum provides a rather model independent way to determine the fragmentation parameterV/P. We determine(V/P) u,d =1.66±0.18±0.63 from the ρ/π ratio and(V/P) s =0.90±0.13±0.27 from theK */K ratio. For thef 2(1270) production we findf 2/ρ0=0.30±0.08.

The density of charged particles near a single highp T trigger particle in proton-proton collisions has been studied with the SFM detector at the CERN-ISR. It is shown that:—the secondaries cluster in a jet-like manner about the trigger particle; —the momentum distributions in this trigger jet can be unambiguously separated from the spectator background distributions;—the momentum distributions in this trigger jet can be unambiguously separated from the spectator background distributions;—the momentum component transverse to the jet direction is exponentially damped;—the width of the transverse distributions increases with the momentum component along the jet;—the fractional longitudinal momentum density depends strongly on the trigger transverse momentum and scales with $$x_T = {{2p_T } \mathord{\left/ {\vphantom {{2p_T } {\sqrt s }}} \right. \kern-\nulldelimiterspace} {\sqrt s }}$$ . All these findings support the hard constituent scattering origin of highp T hadrons.

In pp collisions at √s = 44.7 and 62.3 GeV, where each proton fragments into at least one low-pT, high-x meson or baryon, no correlations between the particle momenta are found for ππ, πK, KK, and pπ pairs. The ππ data show a preference for the formation of electrically neutral ππ systems. The KK data show the influence of strangeness conservation. For pp and pΛ final states, the momentum dependence of the correlation ratio R can be described by the scaling variable z = (1 − x1)(1 − x2). Small deviations from factorization are discussed.

EGS is a very popular Monte Carlo code, used in the simulation of Nuclear Medicine devices. Simulation techniques are particularly effective to optimize collimator configuration and camera design in Single Photon Emission studies. With the EGS code, users must define the geometry where particles are transported. This can be both a very hard task and a source of inefficiency, especially in the case of complex geometries as, for instance, hexagonal hole collimators or pixellated detectors. In this paper we present a modular description of such geometries. Our method allows the computation of the region a point belongs to in a few steps; thus we are able to calculate this region in a reduced number of operations, independently of the collimator and detector dimensions. With a modular description we can reduce the computational time by 30%, with respect to a "traditional" description of the geometry. We validated the modular description in the simulation of a Nuclear Medicine apparatus for scintimammography. Two different collimators have been considered: one with square holes and one with hexagonal holes. We accomplished their characterization and tested their performance in a torso–breast phantom. Outcomes of the two collimators are comparable, even if it seems that the hexagonal hole collimator, thanks to its greater septal penetration, could give slightly better results for small tumors located near the collimator.

I propose experimental tests to examine the origin of the average <p T > dependence on hadron-hadron events multiplicity and to verify some possible implications of the existence of high-temperature phase transition to quark-gluon plasma. I suggest that evidences may already exist of this transition in SPS collider and ISR events with central rapidity density dn/dy within an interval of one or two units around 6.

A novel approach to the detection of masses and clustered microcalcification is presented. Lesion detection is considered as a two-class pattern recognition problem. In order to get an effective and stable representation, the detection scheme codifies the image by using a ranklet transform. The vectors of ranklet coefficients obtained are classified by means of an SVM classifier. Our approach has two main advantages. First it does not need any feature selected by the trainer. Second, it is quite stable, with respect to the image histogram. That allows us to tune the detection parameters in one database and use the trained CAD on other databases without needing any adjustment. In this paper, training is accomplished on images coming from different databases (both digitized and digital). Test results are calculated on images coming from a few FFDM Giotto Image MD clinical units. The sensitivity of our CAD system is about 85% with a false-positive rate of 0.5 marks per image.

We have developed a compact single photon emission mammotomography (SPEMT) scanner capable of imaging the breast for the detection of small size (T1b) tumors. The scanner has a vertical-axis-of-rotation (VAOR) geometry, in which two gamma cameras orbit around a pendulous breast of a prone patient. The SPECT system is rotating around the vicinity of the breast in order to achieve high spatial resolution. The system field-of-view is 147 mm diameter and 41.6 mm height. Each head is made up of one pixilated Nal(Tl) crystal matrix with 2.2 mm pitch and 6 mm thickness coupled to three Hamamatsu H8500 64-anodes PMT's. The measured performance confirm that the system could overcome the present clinical sensitivity limit (about 1 cm diameter) for the detection of small size tumors.

Genetic algorithms are search or classification algorithms based on natural models. They present a high degree of internal parallelism. We developed two versions, differing in the way the population is organized and we studied and compared their characteristics and performances when applied to the optimization of multidimensional function problems. All the implementations are realized on transputer networks.

The meniscus profile between two concentric surfaces has been investigated experimentally and by a computational technique. Unlike previous computational methods, the procedure is directly applicable to a specific set of system parameters. The procedure has been extended to enable the prediction of the meniscus external to a cylinder or a flat plate.

Gluon scattering processes are studied in hadronic highp T events using data obtained with the Split Field Magnet detector (SFM) at the CERN ISR. The experimental set-up allowed the scanning of a wide range of parton energies and scattering angles. It is shown that for positive pions as trigger particles, the parton composition of the recoil jet is correlated with the polar angle and transverse momentum of the triggering pion. Over the kinematical region studied, the recoil jet originates predominantly from scatered gluons, with an increasing prevalence of the gluon component towards forward triggering angles. The variation of the momentum structure of the recoil jet with the trigger angle indicates that the fragmentation function of gluons is softer than that of quarks.

Production of jets with fast leading fragments has been studied in deep inelastic proton-proton interactions using the Split Field Magnet detector at the CERN ISR. The kinematics of the underlying parton processes is determined on an event-by-event basis. Parton scattering amplitudes are extracted for scattering angles, Θ∗, in the interval −0.4 < cos Θ∗ < 0.9. The data which are known to be dominated by quark gluon scattering, agree with predictions from lowest order QCD, while e.g. an abelian theory can be excluded.

Reduction of False Positive signals (FPR) is a fundamental, yet awkward, step in computer aided mass detection schemes. This paper describes preliminary results of a filtering approach to FPR based on Support Vector Regression (SVR), a machine learning algorithm arising from a well-founded theoretical framework, the Statistical Learning Theory, which has recently proved to be superior to the conventional Neural Network framework for both classification and regression tasks: indeed, the proposed filtering method belongs to the family of neural filters. The SVR filter is forced to associate subregions extracted from input images, masses and non-masses, to continuous output values ranging from 0 to 1 representing a measure of the presence in the subregion of a mass. A weighted sum of outputs over each image is used to accomplish the FPR task. In the test phase, this approach reached promising results, retaining 87% of masses while reducing False Positives to 62%.

Several research groups have developed computer-aided diagnosis (CAD) programs for the detection and classification of microcalcifications and masses. For most of these programs, there are some common steps that have to be fulfilled in order to find the suspect lesions. Figure 4.1 shows an example of the typical steps needed for a CAD program. Starting from a digital (or digitized) mammogram, the first operations are the preprocessing ones. Here, the breast is segmented and some filtering or normalization accomplished in order to improve the quality of the image and reduce the noise. Then, a signal extraction step is performed. In this phase, objects similar to the lesions are isolated by means of different techniques. After that, a set of features is calculated on the extracted signals. Basically, researchers have investigated two types of features: those traditionally used by radiologists (gradient-based, intensity-based, and geometric features) and high-order features that may not be as intuitive to radiologists (e.g., texture features). Finally, a classification (false-positive reduction) step is performed, where on the basis of the mentioned features, false signals are separated from the suspect lesions by means of a classifier. In other words, the candidate lesions are first located and then further analyzed in a feature analysis and classification phase to determine the final classification of each candidate. Each stage of most of the CAD schemes uses multiple parameters such as threshold values, filter weights, and region of interest (ROI) sizes. To have a high performance, the values of these parameters need to be selected optimally. In general, however, the optimal set of parameters may change when a component of the imaging chain is modified or changed. This is because some of the parameter values depend implicitly or explicitly on the previous steps. Also, the parameter values have to be redetermined if a new component is added to the CAD scheme for improvement of its performance. Many CAD systems are composed of several independent yet interrelated parts, and some optimization studies have to be done for maximizing the performance. A commonly used approach is to try different combinations of parameters in an ad hoc manner and empirically select the best values based on the test results. However, this manual optimization process searches only very limited regions of the large-dimensional parameter space. In order to overcome the difficulties associated with manual optimization, automated methods have been developed.

In this chapter, the present status and future possibilities for computer-aided-detection (CAD) in breast imaging is considered. Xeromammography and later, conventional x-ray mammography, were among the earliest medical imaging modalities to benefit from the use of computers to assist radiologists in detecting lesions, especially cancers. At present, mammography is the preferred method to screen asymptomatic women for breast cancer. Breast cancer itself is a heterogeneous disease with no cure; the earlier the cancer is detected, the better the prognosis. The principal thrust of CAD research in recent years has therefore been to detect early signs of the disease, such as microcalcifications, small masses, and subtle lesions, especially those most likely to be missed by radiologists, so that any cancer present may be detected at the earliest possible stage of the disease. In this chapter, CAD in breast imaging is reviewed, and the possible lines of future research and development speculated on. The major unresolved problems are identified and, in some cases, promising trends and possible solutions are outlined. Mammography has certain structural deficiencies that have propelled research into alternative imaging modalities for breast cancer detection. Some of these emerging imaging modalities that could either be adjuncts to mammography or supplant it in the future are reviewed. The possible roles for CAD for these alternative modalities are also examined. Certain generic problems of CAD, such as accurate segmentation, registration, lesion detection, and assessment of algorithm performance are then considered. The technology of CAD, in the context of mammography, generally stands for computer-aided detection of lesions and suspicious regions, meriting careful scrutiny by a radiologist. If a patient's history and the radiologist's findings are taken into account, together with the computer-aided detection data that provides diagnostic output, a computer-aided diagnosis (CADx) system exists. Sometimes, a computer-aided diagnosis system is also confusingly referred to by the acronym CAD. In an attempt to overcome such confusion, a computer-aided detection system is sometimes referred to as a CADe system. In this chapter, computer-aided detection is referred to as CAD, and computer-aided diagnosis as CADx.

Data are presented for the first time on inclusive Pomeron-Pomeron interactions which produce a central systemX (composed mainly of multimeson states) in proton-proton collisions at $$\sqrt s $$ at the CERN ISR. The systemX has a Feynman-x distribution which is sharply peaked atx f=0, is inconsistent with any significant contributions from Reggeon exchange processes, and has an invariant mass dependence in good agreement with the predicted formM −2 . Kaon production is about 15% of pion production, nearly independent ofM x, while proton-antiproton production averages about 5% of pion production and increases withM x. The structure of the central systemX develops into a jetlike shape, asM x increases, as would be expected from a model of Pomeron fragmentation. The shape of thex f(π) distribution in the center of mass of theX system (although not proving existence) is consistent with asoft partonic substructure of the Pomeron.

Ratios of inclusive cross sections σ(π+)/σ(π++K ++p) and $$\sigma (\pi ^ - )/\sigma (\pi ^ - + K^ - + \bar p)$$ were measured for proton-proton interactions with a highp T hadron in the final state around c.m.s. scattering angles θ≅20°, 20° and 45° at two ISR energies $$\sqrt s = 31$$ Gev and 62 GeV. Results are shown as functions of transverse and longitudinal momentum and are compared with parton model predictions. The different dependences of positive and negative pion fractions atp T ≅2–3 GeV/c on longitudinal momenta is similar to that observed in soft hadronic interactions at low values ofp T where the leading proton effect (diquark fragmentation) is known to contribute. The quantitative agreement of the data with diquark model predictions indicates the presence of diquark fragmentation also in highp T jets.

10076 Background: The highly heterogeneous clinical courses of B-CLL can be foreseen by investigating IgVH gene mutations or expression of specific prognosticators. Gene and surface-antigen expression profilings (GEP and SEP) have been both employed for identifying molecules of prognostic relevance in B-CLL. Methods: i) GEP - Purified B-CLL cells from 19 UM and 38 mutated (M) cases were investigated for differential GEP by using a two-color Operon Human Genome Oligo Set 2.1 platform with normal B cells as common reference and by applying, after data pre-processing, the LIMMA (Linear Model for MicroArray) package with two combined cutoffs (Bayesian log-odds&gt;1; adjusted p value for false discovery rate &lt;10e−4); ii) SEP - B-CLL cells from 60 UM and 101 M cases were investigated for the expression of 36 surface markers by flow cytometry. Results: i) GEP - 77 probes (32 duplicates) were overexpressed in M B-CLLs (UM/M log-ratio range −0.79/−3.62; p range 6.72e−5/3.6e−10) and 81 probes (46 duplicates) in UM B-CLLs (UM/M log-ratio range 0.87/4.57; p range 9.49e−5/3.01e−12); the CD49d gene was overexpressed in UM cases with UM/M log-ratio of 3.21 (p=8.3e-10). ii) SEP - Six markers significantly discriminated UM to M B-CLLs (t-test, p&lt;10ed-3) and separated most UM (49/60) from M B-CLLs by hierarchical clustering; among them, CD49d was significantly overexpressed in UM cases (median % positive cells 71.2±36 vs. 10.0±31; p=2.1e−10). By applying standardized log-rank statistics in 95 pts, all with CD49d expression and survival data, 30% of positive cells was judged optimal cutoff for identifying two groups with different survivals, 41 CD49dhigh pts showing worse prognosis than 54 CD49dlow cases (p=7.4×10e−5). Similarly, 46 UM B-CLLs had shorter survival than 77 M cases (p=1.4×10e−5). By combining IgVH mutations and CD49d expression, 42 concordant M/CD49dlow pts had better prognosis than 25 concordant UM/CD49dhigh cases (p=1.8×10e−5); noteworthy, among 28 discordant cases, 16 with a M/CD49dhigh phenotype had survival similar to bad prognosis cases. Conclusions: CD49d is a novel prognosticator for B-CLL. Given its high expression level in bad prognosis subsets, CD49d may be a promising therapeutic target. No significant financial relationships to disclose.

Abstract Compact gamma cameras based on arrays of compact Position Sensitive Photomultipliers (PSPMTs) (Hamamatsu R7600–C8/12) were recently developed by several research groups. The previous generation of dedicated gamma cameras (5 in. PSPMT) demonstrated the clinical benefit and general diagnostic value for functional breast imaging in comparison with conventional nuclear medicine technique (Anger Camera prone scintimammography and 99mTc Sestamibi administration). The aim of this paper is to investigate how scintillation material and pixel size of crystal arrays can improve image contrast and tumor SNR values. In this paper we compare tumor Signal-to-Noise Ratio (SNR) results obtained by imagers based on CsI(Tl) and NaI(Tl) array, respectively, by means of a liquid and solid breast phantom. The data collected by NaI(Tl) array show a improvement of SNR values for small tumor size (less than 8 mm). The improvement is also evident in small camera, even though for tumor size less than 6 mm the results are near visibility limit.

A comparison of QCD parton models with events including a high transverse momentum trigger particle is performed. The data were obtained with the Split Field Magnet (SFM) detector at the CERN ISR. The effective intrinsic transverse momentum, 〈k T 〉, of a parton is found to be about 1 GeV/c from an analysis of inclusive cross sections based upon lowest order parton-parton scattering. Large values of 〈k T 〉 are also needed to obtain a good description of the particle density in the azimuthal hemisphere opposite to the trigger. The measured recoil of the spectator jets tends to require much smaller values of 〈k T 〉. The model is improved by including higher-order QCD effects. Thus, starting from 〈k T 〉=0.45 GeV/c, an effective 〈k T 〉 of about 1 GeV/c at hard scattering is generated through perturbative parton showers, which provides a consistent description of the measurements in full phase space. This analysis shows therefore that well-known problems of lowest-order QCD calculations can be resolved by inclusion of higher-order effects.

The average transverse momentum dependence on multiplicity shows in many experiments at center of mass energies ranging from 22 to 7000 GeV a slope change at a charged particle rapidity density constant within systematic uncertainties. We find correlated signals which together with the slope change may indicate a transition to a new mechanism of particles production.

Most of the computer systems developed for the detection or diagnosis of breast lesions share some common steps. Indeed, the typical phases needed for a computer-aided detection (CAD) program are shown in Fig. 7.1. The first operations aim to reduce the noise of the digital mammogram and to isolate suspicious lesions. After that, a feature extraction step is usually carried out. Automatic feature extraction procedures utilize image analysis techniques for the computation of feature vectors characteristic of the segmented lesions. Typically, the lesions are then identified, or characterized, by exploiting the information of the calculated features. Then, a classification phase is performed, where the extracted features are provided as inputs to a classifier. In detection schemes, this phase is known as false-positive reduction. Here, it is necessary to set up a classifier that, hopefully, maintains all the true detected signals, and at the same time, rejects almost all the false positive signals. For diagnosis schemes, this phase aims to characterize the lesion as benign or malignant, on the basis of its features. Several types of classifiers have been investigated, such as artificial neural networks, linear discriminant analysis, and decision trees. Most of the techniques are supervised methods. Recently, a new family of classifiers has appeared in CAD schemes: the support vector machine (SVM). SVMs have been introduced as a technique that relies on statistical learning theory (SLT). Whereas other techniques, e.g., multilayer perceptrons (MLPs), are based on the minimization of the empirical risk, which is the minimization of the number of misclassified vectors of the training set, where SVMs minimize a function that is the sum of two terms. The first term is the empirical risk, the second term (confidence term) controls the ability of the machine to learn any training set without error. SVMs are attracting increasing attention because they rely on a solid statistical foundation and appear to perform quite effectively in many different applications. After training, the separating surface is expressed as a certain linear combination of a given kernel function centered at some of the data vectors (named support vectors). All the remaining vectors of the training set are effectively discarded and the classification of new vectors is obtained solely in terms of the support vectors (SVs). Usually, the smaller the percentage of SVs, the better the generalization of the machine. The interpretative key lies in the fact that, for SVMs, the modeling of the classes is based on the properties of the example vectors at the boundary edge between the two classes. Indeed, what the SVM adds to the other classifiers is a better check of the boundary cases, the ones where it is more difficult to decide whether they belong to one class or to the other. Since it has a sample of examples representing the distribution at the edges of the two classes, the SVM uses these examples for drawing a boundary map between the classes. In this way, much fewer examples are required for carrying out generalizations than would be necessary if it was required to model the entire distribution of the vectors of the class in order to draw out the mean properties. SVMs have been used both in detection and in diagnosis tasks. In both cases, the SVM acts as a classifier that tends to separate two classes of objects: lesions and nonlesions in detection schemes, and benign and malignant (or normal and abnormal) in diagnostic programs. Several studies have demonstrated that in most conditions, the SVM classifier, thanks to its properties, outperforms other type of classifiers.

We have developed a SPEMT (Single Photon Emission MammoTomography) scanner that is made up of two cameras rotating around the pendulous breast of the prone patient, in Vertical Axis of Rotation (VAoR) geometry. Monte Carlo simulations indicate that the device should be able to detect tumours of 8 mm diameter with a tumour/background uptake ratio of 5:1. The scanner field of view is 41.6 mm height and 147 mm in diameter. Each head is composed of one pixilated NaI(Tl) crystal matrix coupled to three Hamamatsu H8500 64-anodes PMT's read out via resistive networks. A dedicated software has been developed to combine data from different PMT's, thus recovering the dead areas between adjacent tubes. A single head has been fully characterized in stationary configuration both in active and dead areas using a point-like source in order to verify the effectiveness of the readout method in recovering the dead regions. The scanner has been installed at the Nuclear Medicine Division of the University of Pisa for its validation using breast phantoms. The very first tomographic images of a breast phantom show a good agreement with Monte Carlo simulation results.

A renewal of eruptive activity at Mt Etna started from the Southeast Crater on 14 July 2006, about 16 months after the end of the last effusive episode. This new eruption reiterated the importance of continuous volcanic monitoring as well as the need of automatic processing and classification of those signals which might be used to disclose such impending eruptive stages. Among seismic signals, volcanic tremor - the persistent background radiation continuously recorded on open conduit, basaltic volcanoes like Mt Etna - is of utmost importance for the identification of different regimes of volcanic activity. Indeed, changes in amplitude and frequency content of volcanic tremor usually herald the unrest of the volcano. The application of the Support Vector Machine classifier to spectrograms of volcanic tremor was carried out on data recorded at Mt Etna in 2001, in a time span of 46 days encompassing episodes of lava fountains and effusive activity. Moving on from the positive results obtained from this automatic classification - with less than 6% of misclassifications - we propose a new application using tools with supervised (Artificial Neural Networks, Support Vector Machine) and unsupervised (Cluster Analysis) learning to the new data set recorded in July 2006. In doing so, we discuss issues such as data transformations for the definition of the patterns, learning and testing strategies as well as the optimization of the classifier configuration (e.g., trial and error, Genetic Algorithms). The performance of each method is analyzed and discussed in terms of identification of the different states of the volcano. Finally, we carry out a careful a-posteriori analysis of the misclassifications, devoting particular attention to their temporal distribution and relation to transitional states of volcanic activity.

In this study, an automatic pattern recognition approach is developed for the classification of volcanic tremor at Mount Etna, Italy. A Support Vector Machine (SVM) classifier is trained by means of a supervised learning algorithm to recognize time series recorded during different states of the volcanic system. The classification of the signal is based on the seismic data recorded at the three-component, broadband station ESPD, located about 6 km southeast from the summit craters. In particular, we analyze the data recorded throughout the 17 July - 9 August, 2001 flank eruption. This episode, with its 23 days-long effusive activity, allows us to investigate thoughtfully the whole development of the volcano unrest. Our analysis covers the time span from 1 July to 15 August, 2001, i.e., it includes several days before the onset and after the end of the flank eruption. Up to 142 time series are extracted as windows of approximately 10 minutes for each component of station ESPD. Then spectrograms are calculated for each time series applying a sliding window technique, and the values obtained averaging the rows of each spectrogram are used as classification features. Following this approach, the frequency content averaged over time is hence used for discriminating different states of activity. In particular, we distinguish four stages, i.e., pre-eruptive, lava fountains, eruptive and post-eruptive. Following a boot-strap strategy, we repeat a random selection of the training set (ca. 80% of the entire data set) and testing set (ca. 20%) 100 times. On the basis of the data set encompassing the three components (426 examples), SVM correctly classifies 94.65 +/- 2.43% of the data. Classification performances can be further improved by reducing the number of classes, namely considering lava fountains as either pre-eruptive or eruptive states depending on their position in time. In this case, SVM correctly classifies 97.25 +/- 1.63% of the data.

Continuous seismic monitoring has achieved a key position in monitoring active volcanoes. However, it comes with the problem of a huge quantity of data difficult to handle. Automatic pattern recognition techniques have proven effective in seismic data processing and, consequently, have been increasingly implemented to solve different tasks. In this paper we investigate the development of the characteristics of the seismic signal on Mt Etna and its relation to regimes of volcanic activity. To this purpose we apply classification methods both with supervisor (Artificial Neural Networks, Support Vector Machine) and without supervisor (cluster analysis). The former learn from exemplar patterns, inferring rules to deal with new and/or noisy data to classify, whereas the latter seek for heterogeneities in the data set applying a specific metric. The choice of automatic classification methods is determined by the necessity to solve rather complex discrimination problems using as little a-priori information as possible. We focus on volcanic tremor recordings at Mt Etna in 2001, a time span where there is a wide variety of feature signals, encompassing periods of pre- and post-eruptive quiescence, episodes of lava fountains, and a 23 day-long effusive activity. We establish four target classes, i.e., pre-eruptive, lava fountains, eruptive, and post-eruptive. The a-priori information used for the classification with supervisor is based on volcanological reports, and therefore it does not directly depend on the characteristics of the seismic signal. We discuss performance and characteristics of the different techniques in light of an implementation to automatically analyze seismic data and reduce volcanic hazard.

This work is developed within the framework of a larger project, which aims to develop a multimodal CT-SPET system dedicated to breast imaging. The goal of this paper is to optimize the choice of the various parameters involved in the design of a SPET system dedicated to breast imaging. In particular, we simulated different collimators, different tumor to background (T/B) ratios for two different spherical tumors with diameters of 5 mm and 8 mm. The performance of the explored cameras were analyzed in terms of SNR and image contrast (IC) values, calculated on the reconstructed images. In addition, we investigated the visibility limits of the system, by modifying the tumor size, the T/B value, and the diameter of the breast phantom (8 cm, 10 cm, and 13 cm). As a general tendency, we found out that a high-resolution camera is preferable, in terms of image contrast. On the other hand, the general purpose collimator seems to give a smoother image, giving rise to SNR values comparable to those obtained with the high-resolution collimator, even with a reduced contrast. High-sensitivity collimators seem to give a worse response on the reconstructed images. The 8 mm tumor is clearly visible for all the simulated conditions, even if it could be very close to the visibility limit for the high-sensitivity collimator. The 5 mm tumor is close to the visibility limit for general purpose and high-resolution collimators, for a T/B ratio equal to 10:1 and is not visible with high-sensitivity collimator. The smaller tumor is almost obscured by the background with the thickest breast (13 cm diameter).

Abstract B-CLL is a apparently homogeneous disease with variable clinical courses, which can be foreseen by the presence of mutated (M) or unmutated (UM) IgVH genes and the expression of prognostic markers, including CD38. Since a correlation between high CD38 and UM IgVH gene configuration has been described, we performed GEP to identify the gene signature of CD38+/UM B-CLLs. Purified (&gt;95%) B-CLL cells from 44 cases were utilized for a dual-labeling GEP strategy (Operon Human Genome 2.1 OligoSet; 21,329 70mers) with pooled normal PB B-cells as common reference. 12 B-CLLs were UM (&lt;2% IgVH mutations) and CD38pos (CD38&gt;30% of B-CLL cells), while 32 were M (&gt;2% IgVH mutations) and CD38neg (CD38&lt;10% of B-CLL cells). To discover genes differentially expressed in the two categories and overcome the problem of unbalanced dataset, we applied an original bioinformatic approach called multi-SAM (Significance Analysis of Microarrays). This consists in reiterated applications of SAM analysis comparing the less populated CD38pos/UM class with 1,000 random samplings, each of 12 cases, from the CD38neg/M class. For each single application of SAM, a list of differentially expressed genes (p&lt;10-3) was generated. At the end of 1,000 reiterations, each single gene was labeled with a 0-1,000 list score (LS) based on the times it was selected by multi-SAM as differentially expressed. A significant LS threshold&gt;300 was determined by applying multi-SAM to 1,000 random comparisons of two mock-classes, each of 12 cases, from the same dataset. The final gene list was further shrunk by keeping only the genes with a median-log-difference (MLD) between the two categories exceeding the absolute value of 1; eventually, a list of 132 genes (44 down-regulated and 88 up-regulated in CD38pos/UM cases) was obtained. According to these analyses, CD38pos/UM B-CLLs overexpressed the following gene groups: i) genes related to lipid metabolism: mainly Lipoprotein Lipase (LS=744, MLD=2.05), but also low-density-lipoprotein receptor (LDLR) and LDLR-related-protein-5, these latter with a LS&gt;300 but lower (0.7) MLDs. ii) genes related to cell-cell/cell-matrix interactions: CD49d/alpha4 integrin (LS=354, MLD=1.14), a molecule whose expression has already been correlated with CD38 in previous extensive surface antigen expression studies of ours; the C-C chemokines MIP-1alpha (a.k.a. CCL3; LS=660, MLD=1.46) and MIP-1beta (a.k.a. CCL4; LS=334, MLD=1.36); CD72 (low-affinity CD100 ligand; LS=523, MLD=1.06). iii) genes related to vescicle trafficking/cytoskeletron reorganization: septin-7 (LS 386, MLD=1.19) and septin-10 (LS=926, MLD=3.12); the spastic paraplegia-20 protein (a.k.a. spartin, LS=886, MLD=1.84); iii) Activation-Induced Cytidine Deaminase (AICD; LS=599, MLD=2.07), a gene preliminarly found as overexpressed in UM B-CLLs. Altogether, these genes, besides having clinical value as additional prognosticators, may be implied in several aspects of the functional cross-talk between CD38pos/UM B-CLL and neighbouring cells within the lymph node microenvironment, this interplay eventually affecting survival of tumor cells.

In this paper a two-class classification problem is faced. One class is constituted by tumoral masses, breast tumors with size ranging from 3 mm to 30 mm. The other class is constituted by non-masses. A Support Vector Machine (SVM) is used as a classifier. Both, masses and non-masses, are extracted from the University of South Florida (USF) mammographic image database and are presented to the classifier as crops with pixel size 64 x 64. In order to find the optimal solution to this problem, different featureless crops representations are evaluated. In particular, a pixel-based representation, a Discrete Wavelet Transform (DWT) representation and an Overcomplete Wavelet Transform (OWT) representation are tested.

Abstract Acute myeloid leukemia (AML) with MLL rearrangements (MLLmut), found mainly in M5 or M4 FAB subtypes, is frequent in infants and secondary leukemias. The most common MLL translocation gives rise to MLL-AF9. MLL protein interacts with histone deacetylases (HDACs) -1 and -2 through the MLL repression domain. We report the effects of HDAC inhibition by valproic acid (VPA) in MLL-AF9 AML-M5 cells (THP-1, MM6 and MOLM-13) and MLLmut AML-M5 blasts. VPA led to histone hyper-acetylation, strong cell-growth inhibition, G1 cell-cycle arrest and apoptosis. Combined treatment with all-trans-retinoic-acid (ATRA) did not substantially improve these effects. VPA increased MLL-AF9 transcription, indicating that VPA overcomes the cell-growth promoting activity and resistance to apoptosis conferred by MLL-AF9 in AML-M5 cells, even with increased MLL-AF9. A small number of genes were significantly affected by VPA in p53-absent THP-1 cells (GeneChip analysis), and the majority of these were up-regulated. VPA potently induced p21 and cyclin G2 (CG2) expression. p21 and CG2 targeted inhibition demonstrated that p21 acts as a key regulator in the VPA-inducted G1 cell-cycle arrest, while induction of CG2 has no effect. These data suggest that these poor prognosis patients may benefit from HDAC inhibitor therapy.

The introduction of a new gamma camera fully dedicated to scintimammography (Single Photon Emission Mammography-SPEM), and more recently with a full breast FoV, allowed to make clinical examination in cranio-caudal projection like in RX-mammography, with breast mildly compressed. Such cameras are based on pixellated scintillation array and position sensitive photomultiplier (PSPMT). Reducing the collimator-tumor distance, the geometric spatial resolution and contrast was enhanced. Unfortunately, due to the scintimammographic low counting, poor contrast images are still obtained, in particular for small tumor. The aim of this paper is to evaluate how a camera based on pixellated detector can improve the SNR values for small tumor by an effective correction of the spatial response. The procedure is based on good pixel identification. A Small Gamma Camera (SGC) was arranged using metal channel dynode PSPMT photomultiplier (Hamamatsu R7600-C8) coupled to different CsI (Tl) scintillator array, with field of view (FoV) with an all purpose collimator. This PSPMT kind drastically reduces the charge spread improving the intrinsic characteristics of the imager. The dimensions of the CsI (Tl) arrays were the same of PSPMT active area (22/spl times/22 mm/sup 2/). Considering the very high intrinsic spatial resolution, a look up table was realized to accurately correct the gain and spatial non-uniformities. We used a breast and torso phantom to characterize the SNR as a function of scintillation pixel size, thickness of the breast, tumor size and depth. The data showed that the SNR depends principally on the match between the tumor and pixel size. In particular, for a 6 mm diameter tumor, the best SNR results were obtained by a 2/spl times/2 mm/sup 2/ pixelled array. For larger tumors, up to 10 mm diameter, a greater pixel size, like 30 mm/sup 2/ or 4/spl times/4 mm/sup 2/, optimizes the SNR value. We compared the results of this camera with the analogous ones obtained by a SPEM gamma camera and by a standard Anger Camera.

We describe here a preliminary upper limit of the cross-section for production of particles with masses around 2 GeV and lifetimes of the order of 10-13 seconds, obtained by using nuclear emulsions exposed to 300 and 400 GeV protons at the Fermilab accelerator.

This paper is centered on the analysis of lateral connections in a network of neurons following BCM synaptic modification theory. This is a non-supervised algorithm, based on properties inspired by the behaviour of real neurons in hyppocampus and visual cortex, that allows synaptic strength to increase or decrease according to a time-varying threshold based on the previous “history” of neural activity. From a statistical point of view it represents a Projection Pursuit (PP) method based on an Objective Function that seeks “interesting” projections in input space, along those directions presenting statistical properties far from normal (gaussian). After a short introduction to BCM theory, we test various schemes of lateral connectivity between neurons receiving the same inputs, using assumptions inspired by the anatomical and functional properties of visual cortex of evolved mammals (like primates and cats). Using computer simulations, the different schemes (uniform, random, gaussian, coulombian, linearly and exponentially decaying connections) were compared in relation to their ability to improve selectivity and to form a “metric”, namely to preserve the correspondence between close inputs and the topology of the corresponding activated neurons, in the case of linearly dependent inputs.

In this paper we present an expert system to make the real time diagnosis of an experimental apparatus. To overcome the drawbacks due to the time constraints required by a real time expert system a diagnostic apparatus based on a transputer network has been developed. The parallelization involves different parts of the diagnostic system: (1) the sensor data acquisition (2) the data interpretation, (3) the expert diagnosis. VME and PC boards have been used and the software has been written in Occam, parallel C and parallel Prolog. Experimental results of the system performances are presented.