Breast Lump Recognition Algorithms Based on Ultrasound Radio-Frequency Signals
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Abstract:
A method for evaluating the benign and malignant breast tumors based on radio-frequency (RF) data was explored by extracting the characteristic parameters of breast ultrasound RF signals. The breast biopsy data were used as the reference data for judging the lump benign or malignant. The extracted ultrasound RF data were reconstructed and segmented by computer aided method to obtain the breast tumor region of interest (ROI) and its characteristic parameters (entropy and standard deviation). The characteristic parameters were statistically analyzed to evaluate the relationship between characteristic parameters and benign or malignant breast tumors. The results indicate the entropy and standard deviation of normal region is much higher than that of lump region, which shows that the standard deviation and entropy characteristic parameters of ultrasonic RF signals are meaningful in the diagnosis of breast tumors. The proposed method provides a new direction for computer-aided diagnosis of benign and malignant breast tumors.
YAN Yu, CAI Xiaowei, ZHU Wei, CAI Runqiu, WU Yiyun. Breast Lump Recognition Algorithms Based on Ultrasound Radio-Frequency Signals[J]. Transactions of Nanjing University of Aeronautics & Astronautics,2019,36(4):635-640