@inproceedings{271344b4e24f4f048e581ba391cd2c52,
title = "Automatic Classification of Breast Tissue",
abstract = "A recent trend in digital mammography are CAD systems, which are computerized tools designed to help radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast is increased. In addition, the suitability of abnormality segmentation approaches tends to depend on breast tissue density. In this paper we propose a new approach to the classification of mammographic images according to the breast parenchymal density. Our classification is based on gross segmentation and the underlying texture contained within the breast tissue. Robustness and classification performance are evaluated on a set of digitized mammograms, applying different classifiers and leave-one-out for training. Results demonstrate the feasibility of estimating breast density using computer vision techniques.",
keywords = "breast tissue, breast density, digital mammography, pectoral muscle, confusion matrice",
author = "Arnau Oliver and Jordi Freixenet and Anna Bosch and David Raba and Reyer Zwiggelaar",
year = "2005",
month = may,
day = "23",
doi = "10.1007/11492542_53",
language = "English",
isbn = "978-3-540-26154-4",
volume = "3523",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "431--438",
editor = "Marques, {Jorge S.} and {P{\'e}rez de la Blanca}, Nicol{\'a}s and Pedro Pina",
booktitle = "Pattern Recognition and Image Analysis",
address = "Switzerland",
edition = "II",
note = "Pattern Recognition and Image Analysis : Second Iberian Conference, IbPRIA 2005, ; Conference date: 07-06-2005 Through 09-06-2005",
}