Automatic segmentation of mammograms using a Scale-Invariant Feature Transform and K-means clustering algorithm

In this work, a Scale-Invariant Feature Transform method, together with a K-means clustering is used in order to find regions of interest (ROI's) in mammograms. This paper focuses on presenting a tool that can improve the search of suspicious areas that contain abnormalities, leaving the final decision to the radiologist. The methodology is divided into three sections: first, a pre-processing step that consist in acquiring image and reduction its size erasing the background leaving only the breast area and eliminating noise. The second step is to improve the image quality through image thresholding and histogram equalization limited contrast (CLAHE). Last step of the methodology is the location of regions of interest in the image and is done using Scale-Invariant Feature Transform (SIFT) as the main tool and is complemented with Binary Robust Independent Elementary Features (BRIEF) to find descriptors and as classifier K-Means Clustering. Finally in the results are presented the location of ROI's and they are compared with the position of abnormalities diagnosed by the Mammographic Image Analysis Society.