Features Extraction and Fuzzy Logic based Classification for False Positives Reduction in Mammographic Images

Breast cancer is one of the most common neoplasms in women and it is a leading cause of death worldwide. A proper screening procedure can help an early diagnosis of the tumor so reducing the death risk. A suitable computer aided detection system can help the radiologist to detect many subtle signs, normally missed during the screening phase, submitting to the radiologist’s attention those regions that could contain an abnormality. However, one of the most critical problem deals with a suitable tradeoff regarding the number of suspicious zones to present to the radiologist and the capability of identifying the correct ones. In this work, the classification of suspicious signs into normal tissue or massive lesions has been faced in order to get a False Positive Reduction without noticeably affecting the number of True Positives.

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