TY - GEN
T1 - A Comparison of Breast Tissue Classification Techniques
AU - Oliver, Arnau
AU - Freixenet, Jordi
AU - Marti, Robert
AU - Zwiggelaar, Reyer
PY - 2006/9/21
Y1 - 2006/9/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79551681197&partnerID=8YFLogxK
U2 - 10.1007/11866763_107
DO - 10.1007/11866763_107
M3 - Conference Proceeding (Non-Journal item)
SN - 354044727X
SN - 9783540447276
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 872
EP - 879
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings
PB - Springer Nature
ER -
