USING DSIFT AND LCP FEATURES FOR DETECTING BREAST LESIONS.

This research promises a two class classification of digitized mammogram images using two feature extraction algorithms; Dense Scale Invariant Feature Transform (DSIFT) and Linear Configuration Pattern (LCP). The dataset that is used for this research is retrieved from Image Retrieval in Medical Applications (IRMA) project which provides mammographic patches consisting of normal, benign and malignant cases of publicly available digital mammographic databases. In this research, 200 normal and 200 abnormal lesion cases of Digital Database for Screening Mammography (DDSM) are used. Both linear configuration pattern (LCP) and DSIFT feature vectors have a high accuracy in classifying normal cases versus abnormal up to 100%. This approach can be used in computer-aided diagnosis to help radiologists to distinguish a healthy case from others in an efficient way. [ABSTRACT FROM AUTHOR]