A mass classification using spatial diversity approaches in mammography images for false positive reduction

• Methodology for describing patterns using diversity indexes associated with spatial decompositions.
• Influence of location in describing texture regions extracted from mammograms.
• False positive reduction of 100% when tested with a DDSM ROI database.

Breast cancer is configured as a public health problem that affects mainly women population. One of the main ways of prevention is through screening mammography. The interpretation made by the physician is a repetitive task because of a low contrast image and the examination of several exams. So, computer systems have been proposed to aid detection step and helps physician, with the aim to increase sensitivity at the same time that reduces invasive procedures. Although these systems had improved the sensitivity of the original examination of mammography, they also generate a lot of false positives. This paper presents a methodology for reducing false positives by analyzing the diversity of approaches with improved spatial decomposition. After experiments the results reaches a high level of sensitivity at the same time promote a high rate of reduction of false positives.