Abstract
It is widely accepted in the medical community that breast tissue density is an important risk factor for the development of breast cancer. Thus, the development of reliable automatic methods for classification of breast tissue is justified and necessary. Although different approaches in this area have been proposed in recent years, only a few are based on the BIRADS classification standard. In this paper we review different strategies for extracting features in tissue classification systems, and demonstrate, not only the feasibility of estimating breast density using automatic computer vision techniques, but also the benefits of segmentation of the breast based on internal tissue information. The evaluation of the methods is based on the full MIAS database classified according to BIRADS categories, and agreement between automatic and manual classification of 82% was obtained.
| Original language | English |
|---|---|
| Title of host publication | Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings |
| Subtitle of host publication | 9th International Conference, Copenhagen, Denmark, October 1-6, 2006, Proceedings, Part II |
| Publisher | Springer Nature |
| Pages | 872-879 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-3-540-44728-3 |
| ISBN (Print) | 354044727X, 9783540447276 |
| DOIs | |
| Publication status | Published - 21 Sept 2006 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4191 LNCS - II |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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