Computer Aided Diagnosis System for Breast Cancer Based on Color Doppler Flow Imaging

Authors:
Yan Liu;H. D. Cheng;J. H. Huang;Y. T. Zhang;X. L. Tang;J. W. Tian;Y. Wang
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, People's Republic of China 150001 and College of Science, Harbin Institute of Technology, Harbin, People's Republ ...;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, People's Republic of China 150001 and Department of Computer Science, Utah State University, Logan, USA 84322;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, People's Republic of China 150001;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, People's Republic of China 150001;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, People's Republic of China 150001;Second Affiliated Hospital of Harbin Medical University, Harbin, China;Second Affiliated Hospital of Harbin Medical University, Harbin, China
Venue:
Journal of Medical Systems
Year:
2012

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Abstract

Color Doppler flow imaging takes a great value in diagnosing and classifying benign and malignant breast lesions. However, scanning of color Doppler sonography is operator-dependent and ineffective. In this paper, a novel breast classification system based on B-Mode ultrasound and color Doppler flow imaging is proposed. First, different feature extraction methods were used to obtain the texture and geometric features from B-Mode ultrasound images. In color Doppler feature extraction stage, several spectrum features are extracted by applying blood flow velocity analysis to Doppler signals. Moreover, a velocity coherent vector method is proposed based on color coherence vector, which is helpful for designing to the optimize detection of flow indices from different blood flow velocity fields automatically. Finally, a support vector machine classifier with selected feature vectors is used to classify breast tumors into benign and malignant. The experimental results demonstrate that the proposed computer-aided diagnosis system is useful for reducing the unnecessary biopsy and death rate.