Classification of mammographic lesions into BI-RADS™ shape categories using the Beamlet Transform

摘要

We present a new algorithm and preliminary results for classifying lesions into BI-RADS shape categories:round, oval, lobulated, or irregular. By classifying masses into one of these categories, computer aideddetection (CAD) systems will be able to provide additional information to radiologists. Thus, such a toolcould potentially be used in conjunction with a CAD system to enable greater interaction andpersonalization. For this classification task, we have developed a new set of features using the Beamlettransform, which is a recently developed multi-scale image analysis transform. We trained a k-NearestNeighbor classifier using images from the Digital Database for Digital Mammography (DDSM). Themethod was tested on a set of 25 images of each type and we obtained a classification accuracy of 78% forclassifying masses as oval or round and an accuracy of 72% for classifying masses as lobulated or round.