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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Oliver, A. Freixenet, J. Zwiggelaar, R. |
| Copyright Year | 2005 |
| Description | Author affiliation: Comput. Vision & Robotics Group, Girona Univ., Spain (Oliver, A.; Freixenet, J.) |
| Abstract | A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist 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 increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques. |
| File Size | 266510 |
| File Format | |
| ISBN | 0780391349 |
| DOI | 10.1109/ICIP.2005.1530291 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-09-14 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Breast cancer Mammography Computer aided diagnosis Image segmentation Lesions Computer vision Robot vision systems Robotics and automation Computer science Data mining |
| Content Type | Text |
| Resource Type | Article |
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