2026-04-17T00:57:31Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/23142024-05-22T09:45:58Zcom_2072_452955com_2072_2054col_2072_452957

00925njm 22002777a 4500 dc Bosch Rué, Anna author Muñoz Pujol, Xavier author Oliver i Malagelada, Arnau author Martí Bonmatí, Joan author 2006 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/10256/2314 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 Modeling and Classifying Breast Tissue Density in Mammograms