Chest x-ray characterization: from organ identification to pathology categorization
Proceedings of the international conference on Multimedia information retrieval
Computer Methods and Programs in Biomedicine
Towards the improvement of textual anatomy image classification using image local features
MMAR '11 Proceedings of the 2011 international ACM workshop on Medical multimedia analysis and retrieval
A comparison of breast tissue classification techniques
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Breast density classification to reduce false positives in CADe systems
Computer Methods and Programs in Biomedicine
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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.