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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.