15 May 2003 Computerized detection and classification of lesions on breast ultrasound
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We are developing a computerized method that detects suspicious areas on ultrasound images, and then distinguishes between malignant and benign-type lesions. The computerized scheme identifies potential lesions based on expected lesion shape and margin characteristics. All potential lesions are subsequently classified by a Bayesian neural net based on computer-extracted lesion features. The scheme was trained on a database of 400 cases (757 images) - consisting of complex cysts, benign and malignant lesions - and tested on a comparable database of 458 cases (1740 images) including 578 normal images. We investigated the performances of lesion detection and subsequent classification by a Bayesian neural net for two tasks. The first task was the distinction between actual lesions and false-positive (FP) detections, and the second task the distinction between actual malignant lesions and all detected lesion candidates. In training, the detection and classification method obtained an Az value of 0.94 in the distinction of false-positive detections from actual lesions, and an Az of 0.91 was obtained on the testing database. The task of distinguishing malignant lesions from all other detections (false-positives plus all benign type lesions) showed to be more challenging and Az values of 0.87 and 0.81 were obtained during training and testing, respectively. For the testing database, the combined detection and classification scheme correctly identified lesions in 82% (0.45 FP per image) of all the patients, and in 100% (0.43 FP malignancies per image) of the cancer patients.
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Karen Drukker, Karen Drukker, Maryellen Lissak Giger, Maryellen Lissak Giger, Carl J. Vyborny, Carl J. Vyborny, Robert A. Schmidt, Robert A. Schmidt, Ellen B. Mendelson, Ellen B. Mendelson, Michael Stern, Michael Stern, } "Computerized detection and classification of lesions on breast ultrasound", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480856; https://doi.org/10.1117/12.480856

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