Diagnosis and Differential Diagnosis of Breast Calcifications

Lanyi, Marton

doi:10.1007/978-3-642-71493-1

Cited by 75 publications

(51 citation statements)

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“…Les radiologues basent donc leur diagnostic sur des caractéristiques 2D. Or ces mêmes radiologues pensent qu'il existe également un lien entre la forme 3D du foyer et le type de la lésion qui est bénigne ou maligne (Lanyi, 1986). Très peu de travaux ont été consacrés à l'obtention d'une représentation 3D du foyer (Yam et al, 2001 ;Gresson, 1998 ;Gresson et al, 1998 ;Maidment et al, 1996).…”

Section: Une Nouvelle Méthode D'imagerie Pour L'aide Au Diagnostic : unclassified

De nouvelles modalités de traitement avec de nouvelles modalités d’imagerie

Noël¹,

Daul²,

Aletti³

et al. 2006

Radioprotection

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RÉSUMÉ Historique et introductionLa radiothérapie a débuté avec les découvertes des physiciens Curie, Becquerel et Röntgen. La radiothérapie comporte deux modalités d'irradiation bien distinctes. La première consiste à implanter dans ou au contact de la lésion des sources radioactives naturelles puis artificielles, il s'agit de la curiethérapie, la seconde utilise des systèmes externes au patient qui produisent des rayons X ou des rayonnements gamma, on parle alors de radiothérapie externe. Depuis le début du 20 e siècle, tous ces systèmes d'irradiation ont considérablement évolué.• En curiethérapie, les sources radioactives naturelles comme le radium ont été remplacées par des sources radioactives artificielles comme le césium-137, l'iridium-192, l'iode-125, etc Article published by EDP Sciences and available at http://www.edpsciences.org/radiopro or http://dx

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Section: Une Nouvelle Méthode D'imagerie Pour L'aide Au Diagnostic : unclassified

De nouvelles modalités de traitement avec de nouvelles modalités d’imagerie

Noël¹,

Daul²,

Aletti³

et al. 2006

Radioprotection

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“…In order for S-Cognitron to produce an optimal feature set for classification, we follow the criteria proposed from the Breast Imaging Reporting and Data System (BI-RADS) which was developed by the American College of Radiology (1993Radiology ( , 1995 and Lanyi (1986). These criteria are based on the numbers of MCCs, morphologic appearances, arrangements, concentrations, distributed sizes and densities, etc.…”

Section: Feature Formation Layer Smentioning

confidence: 99%

Classification of clustered microcalcifications using a Shape Cognitron neural network

et al. 2003

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“…1 One important early sign of breast cancer in mammograms is the appearance of clustered microcalcifications (MCs), which are found in 30%-50% of mammographically diagnosed cases. 2 MCs are tiny calcium deposits which appear as bright spots in mammograms. While often seen, individual MCs can be difficult to detect, because of their subtlety in appearance, variation in shape and size, and inhomogeneity in surrounding tissue.…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model

2015

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Purpose: In computer-aided detection of microcalcifications (MCs), the detection accuracy is often compromised by frequent occurrence of false positives (FPs), which can be attributed to a number of factors, including imaging noise, inhomogeneity in tissue background, linear structures, and artifacts in mammograms. In this study, the authors investigated a unified classification approach for combating the adverse effects of these heterogeneous factors for accurate MC detection. Methods: To accommodate FPs caused by different factors in a mammogram image, the authors developed a classification model to which the input features were adapted according to the image context at a detection location. For this purpose, the input features were defined in two groups, of which one group was derived from the image intensity pattern in a local neighborhood of a detection location, and the other group was used to characterize how a MC is different from its structural background. Owing to the distinctive effect of linear structures in the detector response, the authors introduced a dummy variable into the unified classifier model, which allowed the input features to be adapted according to the image context at a detection location (i.e., presence or absence of linear structures). To suppress the effect of inhomogeneity in tissue background, the input features were extracted from different domains aimed for enhancing MCs in a mammogram image. To demonstrate the flexibility of the proposed approach, the authors implemented the unified classifier model by two widely used machine learning algorithms, namely, a support vector machine (SVM) classifier and an Adaboost classifier. In the experiment, the proposed approach was tested for two representative MC detectors in the literature [difference-of-Gaussians (DoG) detector and SVM detector]. The detection performance was assessed using free-response receiver operating characteristic (FROC) analysis on a set of 141 screen-film mammogram (SFM) images (66 cases) and a set of 188 full-field digital mammogram (FFDM) images (95 cases). Results: The FROC analysis results show that the proposed unified classification approach can significantly improve the detection accuracy of two MC detectors on both SFM and FFDM images. Despite the difference in performance between the two detectors, the unified classifiers can reduce their FP rate to a similar level in the output of the two detectors. In particular, with true-positive rate at 85%, the FP rate on SFM images for the DoG detector was reduced from 1.16 to 0.33 clusters/image (unified SVM) and 0.36 clusters/image (unified Adaboost), respectively; similarly, for the SVM detector, the FP rate was reduced from 0.45 clusters/image to 0.30 clusters/image (unified SVM) and 0.25 clusters/image (unified Adaboost), respectively. Similar FP reduction results were also achieved on FFDM images for the two MC detectors. Conclusions: The proposed unified classification approach can be effective for discriminating MCs from FPs caused by different factors (su...

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Diagnosis and Differential Diagnosis of Breast Calcifications

Cited by 75 publications

References 0 publications

De nouvelles modalités de traitement avec de nouvelles modalités d’imagerie

De nouvelles modalités de traitement avec de nouvelles modalités d’imagerie

Classification of clustered microcalcifications using a Shape Cognitron neural network

Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model

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