A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

Wenqing Sun; Tzu-Liang B. Tseng; Bin Zheng; Jianying Zhang; Wei Qian

doi:10.1117/12.2076633

20 March 2015 A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

Wenqing Sun, Tzu-Liang B. Tseng, Bin Zheng, Jianying Zhang, Wei Qian

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 941422 (2015) https://doi.org/10.1117/12.2076633
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

A novel breast cancer risk analysis approach is proposed for enhancing performance of computerized breast cancer risk analysis using bilateral mammograms. Based on the intensity of breast area, five different sub-regions were acquired from one mammogram, and bilateral features were extracted from every sub-region. Our dataset includes 180 bilateral mammograms from 180 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including sub-region segmentation, bilateral feature extraction, feature selection, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under the curve (AUC) is 0.763 ± 0.021 when applying the multiple sub-region features to our testing dataset. The positive predictive value and the negative predictive value were 0.60 and 0.73, respectively. The study demonstrates that (1) features extracted from multiple sub-regions can improve the performance of our scheme compared to using features from whole breast area only; (2) a classifier using asymmetry bilateral features can effectively predict breast cancer risk; (3) incorporating texture and morphological features with density features can boost the classification accuracy.

Citation Download Citation

Wenqing Sun, Tzu-Liang B. Tseng, Bin Zheng, Jianying Zhang, and Wei Qian "A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941422 (20 March 2015); https://doi.org/10.1117/12.2076633

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