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Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

© IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 2, p. 1552-1558

IEEE

Author: Bosch Rué, Anna
Muñoz Pujol, Xavier
Oliver i Malagelada, Arnau
Martí Bonmatí, Joan
Date: 2006
Abstract: We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal
Format: application/pdf
Citation: Bosch Rué, A., Muñoz, X., Oliver, A., i Martí, J. (2006). Modeling and Classifying Breast Tissue Density in Mammograms. IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 2006, 2, 1552-1558. Recuperat 19 maig 2010, a: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1640941
ISBN: 0-7695-2597-0
ISSN: 1063-6919
Document access: http://hdl.handle.net/10256/2314
Language: eng
Publisher: IEEE
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/CVPR.2006.188
Articles publicats (D-ATC)
Is part of: © IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 2, p. 1552-1558
Rights: Tots els drets reservats
Subject: Diagnòstic per la imatge
Imatges -- Processament -- Tècniques digitals
Imatgeria mèdica – Processament -- Tècniques digitals
Mama -- Radiografia
Radiografia mèdica -- Tècniques digitals
Breast -- Radiography
Diagnostic imaging
Image processing -- Digital techniques
Imaging systems in medicine -- Digital techniques
Radiography, Medical -- Digital techniques
Title: Modeling and Classifying Breast Tissue Density in Mammograms
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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