Abstract
A computerized classification of breast tumor based on B-mode ultrasound and color Doppler flow imaging is proposed. First, the boundary of the breast tumor was manually delineated. Second, five contour features and two gray level features of the tumors were extracted from the B-mode ultrasonic images. Third, an optimal feature vector was created using K-means cluster algorithm. Then a back propagation (BP) artificial neural network (ANN) was used to classify breast tumors as benign, malignant or uncertain. Finally, the blood flow feature was extracted from the color Doppler flow image, which was used to classify the uncertain as benign or malignant. Experiments on 500 cases show that the proposed system yields the accuracy of 100% for the malignant and 80.8% for the benign. According to the result, our system can be used to reduce unnecessary biopsies.
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