29 April 2005 Classification of mammographic lesions into BI-RADS shape categories using the beamlet transform
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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 aided detection (CAD) systems will be able to provide additional information to radiologists. Thus, such a tool could potentially be used in conjunction with a CAD system to enable greater interaction and personalization. For this classification task, we have developed a new set of features using the Beamlet transform, which is a recently developed multi-scale image analysis transform. We trained a k-Nearest Neighbor classifier using images from the Digital Database for Digital Mammography (DDSM). The method was tested on a set of 25 images of each type and we obtained a classification accuracy of 78% for classifying masses as oval or round and an accuracy of 72% for classifying masses as lobulated or round.
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Mehul P. Sampat, Mehul P. Sampat, Alan Conrad Bovik, Alan Conrad Bovik, Mia K. Markey, Mia K. Markey, } "Classification of mammographic lesions into BI-RADS shape categories using the beamlet transform", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.596563; https://doi.org/10.1117/12.596563

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