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MICROCALCIFICATION EVALUATION IN COMPUTER ASSISTED DIAGNOSIS FOR DIGITAL MAMMOGRAPHY

by Joan Marti, Joan Batlle, Xevi Cufi, Josep Espaiio
"... In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is often associated with a high incidence of breast cancers. Accurate classification into benign and m ..."
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based on a set of shape-based features extracted from the digitised mammography. The segmentation of the microcalcifications is performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes

PSO-KNN Heuristic Parameter Selection and GLCM Features

by Imad Zyout Phd
"... Texture-based computer-aided diagnosis (CADx) of microcalcification clusters is more robust than the state-of-art shape-based CADx because the performance of shape-based approach heavily depends on the effectiveness of microcalcification (MC) segmentation. This paper presents a texture-based CADx th ..."
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that consists of two stages. The first one characterizes MC clusters using texture features from gray-level co-occurrence matrix (GLCM). In the second stage, an embedded feature selection based on particle swarm optimization and a k-nearest neighbor (KNN) classifier, called PSO-KNN, is applied to simultaneously

CLASSIFICATION OF MC CLUSTERS IN DIGITAL MAMMOGRAPHY VIA HARALICK DESCRIPTORS AND HEURISTIC EMBEDDED FEATURE SELECTION METHOD

by Imad Zyout Phd, Christina Jacobs Md
"... Characterizing the texture of mammographic tissue is an efficient and robust tool for the diagnosis of microcalcification (MC) clusters in mammography because it does not require a prior MC segmentation stage. This work is not only intended to validate MCs ’ surrounding tissue hypothesis that reveal ..."
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that reveals the potential of breast tissue surrounding MCs to diagnose microcalcifications, but to present an improvement over the existing methods by introducing a new heuristic feature selection based on particle swarm optimization and KNN classifier (PSO-KNN). Using MC clusters from mini-MIAS and a local

Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

by Imad Zyout
"... Feature selection and classifier hyper-parameter optimization are important stages of any computer-aided diagnosis (CADx) system for mammography. The optimal selection for shape features, kernel parameter, and classifier regularization constant is crucial to achieve a good generalization and perform ..."
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the dimensionality of the input feature space but also optimizes hyper-parameters of the classifier. The performance of the proposed shape-based CADx including PSO-LSSVM parameter selection method is examined using 60 microcalcification clusters. Using different cross-validation procedures, the proposed PSO

A Texture Analysis Approach for Characterizing Microcalcifications on Mammograms

by Anna N. Karahaliou, Ioannis S. Boniatis, Spyros G. Skiadopoulos, Eleni Likaki, George S. Panayiotakis, Lena I. Costaridou
"... Abstract—The current study investigates whether texture properties of the tissue surrounding microcalcification (MC) clusters can contribute to breast cancer diagnosis. The case sample analyzed consists of 100 mammographic images, originating from the Digital Database for Screening Mammography (DDSM ..."
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Abstract—The current study investigates whether texture properties of the tissue surrounding microcalcification (MC) clusters can contribute to breast cancer diagnosis. The case sample analyzed consists of 100 mammographic images, originating from the Digital Database for Screening Mammography

882 Computer-aided classification of breast microcalcification clusters: Merging of features from image processing and radiologists

by Joseph Y. Lo, Marios Gavrielides, Mia K. Markey, Jonathan L. Jesneck
"... We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters, which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification clust ..."
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We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters, which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification

What Diagnostic Role Does the Shape of the Individual Microcalcifications Play Compared

by I. Leichter, R. Lederman, S. S. Buchbinder, B. Novak, S. Fields , 2003
"... with the Geometry of the Cluster? OBJECTIVE. The objective of this study was to compare the diagnostic role of features reflecting the geometry of clusters with features reflecting the shape of the individual microcalcification in a mammographic computer-aided diagnosis system. MATERIALS AND METHODS ..."
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with the Geometry of the Cluster? OBJECTIVE. The objective of this study was to compare the diagnostic role of features reflecting the geometry of clusters with features reflecting the shape of the individual microcalcification in a mammographic computer-aided diagnosis system. MATERIALS

unknown title

by unknown authors
"... important early sign of breast cancer in women. In this paper an approach is proposed to develop a Computer-Aided Diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications ’ patterns in digitized mammograms earlier and faster than typical screening programs. T ..."
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the important features of each cluster and (c) the classification stage, which classify between normal and microcalcifications’ patterns and then classify between benign and malignant microcalcifications. In classification stage, four methods were used, the voting K-Nearest Neighbor classifier (K-NN), Support

POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES PAR

by Dominique Tschopp, Prof E. Telatar, Prof S. Diggavi, Prof M. Grossglauser, Prof J. -y, Le Boudec, Prof M. Mitzenmacher, Prof S. Shakkottai
"... 2010 to my wife, Joyce, and my family...- Résumé- ..."
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2010 to my wife, Joyce, and my family...- Résumé-

Active Mask Framework for Segmentation of Fluorescence Microscope Images

by Gowri Srinivasa, Advisor Prof, Prof Matthew, C. Fickus, Prof Adam, D. Linstedt, Prof Robert, F. Murphy
"... m]]l]]s¶D]]¿÷mB]iv]b]oD]m¶¨]iv]§]iv]r]j]t¿rv]]irj]]t]]m] / | ap]]r¿]ÎNy]s¶D]]mb¶r]ix} Û]Ix]]rd]mb]} p—N]t]o%ism] in]ty]m] / || Û]Is]¡uÎc]rN]]riv]nd]p]*N]m]st¶ I always bow to Śri ̄ Śāradāmbā, the limitless ocean of the nectar of compassion, who bears a rosary, a vessel of nectar, the symbol of ..."
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facilitated the task of understanding complex sys-tems at cellular and molecular levels in recent years. Segmentation, an important yet dif-ficult problem, is often the first processing step following acquisition. Our team previously demonstrated that a stochastic active contour based algorithm together
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