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Highly Sensitive Computer Aided Diagnosis System for Breast Tumor Based on Color Doppler Flow Images

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Abstract

A computer-aided diagnosis (CAD) system for breast tumor based on color Doppler flow images is proposed. Our system consists of automatic segmentation, feature extraction, and classification of breast tumors. First, the B-mode grayscale image containing anatomical information was separated from a color Doppler flow image (CDFI). Second, the boundary of the breast tumor was automatically defined in the B-mode image and then morphologic and gray features were extracted. 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 selectively from the CDFI, and was used to classify the uncertain tumor as benign or malignant. Experiments on 500 cases show that the proposed system yields an accuracy of 100% for the malignant and 80.8% for the benign classification. Comparing with other systems, the advantage of our system is that it has a much lower percentage of malignant tumor misdiagnosis.

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Acknowledgements

We are grateful for the support of Yulan Wang and Yan Luo, who have given us much help on the selection of images and analysis of the results. This study was supported by the Natural Science Foundation of China (No. 60772147), Natural Science Foundation of Guangdong Province (No. 9451806001002479).

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Authors and Affiliations

  1. Department of Biomedical Engineering, Shenzhen University, Shenzhen, China

    Xian-Fen Diao, Xin-Yu Zhang, Tian-Fu Wang, Si-Ping Chen, Ying Yang & Ling Zhong

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  1. Xian-Fen Diao
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  2. Xin-Yu Zhang
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  3. Tian-Fu Wang
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  4. Si-Ping Chen
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  5. Ying Yang
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  6. Ling Zhong
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Correspondence to Tian-Fu Wang.

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Diao, XF., Zhang, XY., Wang, TF. et al. Highly Sensitive Computer Aided Diagnosis System for Breast Tumor Based on Color Doppler Flow Images. J Med Syst 35, 801–809 (2011). https://doi.org/10.1007/s10916-010-9461-8

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  • DOI: https://doi.org/10.1007/s10916-010-9461-8

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