Ítem
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Bosch Rué, Anna
Muñoz Pujol, Xavier Oliver i Malagelada, Arnau Martí Bonmatí, Joan |
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| 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 | |
| http://hdl.handle.net/2072/59020 | |
| eng | |
| IEEE | |
| Tots els drets reservats | |
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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 |
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| Modeling and Classifying Breast Tissue Density in Mammograms | |
| info:eu-repo/semantics/article | |
| Recercat |
