Data mining framework for breast lesion classification in shear wave ultrasound: A hybrid feature paradigm. (March 2017)

• Record Type: Journal Article
• Authors: Acharya, U. Rajendra; Ng, Wei Lin; Rahmat, Kartini; Sudarshan, Vidya K.; Koh, Joel E.W.; Tan, Jen Hong; Hagiwara, Yuki; Yeong, Chai Hong; Ng, Kwan Hoong
• Is Part Of: Biomedical signal processing and control. Volume 33(2017)
• Page Start: 400
• Page End: 410
• Publication Date: 2017-03
• Subjects: Elastography -- Shear wave elastography -- Benign breast lesions -- Malignant breast lesions -- Nonlinear features; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.11.004
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: Characterization of benign and malignant breast lesions using shear wave images. Texture features are extracted from discrete wavelet transform coefficients. Obtained classification sensitivity of 90.41% and specificity of 96.39%. Breast cancer risk index is proposed to discriminate lesions using one integer. Abstract: Assessment of elasticity parameters of breast using ultrasound elastography (USE) provides exclusive information about the cancerous tissue. Shear wave elastography (SWE), a new USE imaging procedure is increasingly used for elasticity evaluation of breast lesions. SWE examination is gaining popularity in the characterization of benign and malignant breast lesions as it has high diagnostic performance accuracy. However, some degree of manual errors, such as probe compression or movement may cause inaccurate results. In addition, the systems cannot measure elasticity values in small lesions where the tissues do not vibrate enough. Thus, computer-aided methods suppress these technical or manual limitations of SWE during evaluation of breast lesions. Therefore, this paper proposes, a novel methodology for characterization of benign and malignant breast lesions using SWE. Original SWE image is subjected to three levels of Discrete wavelet transform (DWT) to obtain different coefficients. Second order statistics (Run Length Statistics) and Hu's moments features are extracted from DWT coefficients. Extracted features are subjected to sequential forward selection (SFS) method to obtain the significant features and ranked using ReliefF feature ranking technique. Ranked features are fed to different classifiers for automated characterization of benign and malignant breast lesions. Our proposed technique achieved a significant accuracy of 93.59%, sensitivity of 90.41% and specificity of 96.39% using only three features. In addition, a unique integrated index named Shear Wave Breast Cancer Risk Index (sBCRI) is formulated for characterization of malignant and benign breast lesion using only two features. The proposed index, sBCRI, provides a single number which characterizes the malignant and benign cancer faster. This system can be employed as an ideal screening tool as it has high sensitivity and low false-positive rate. Hence, the women with benign lesions need not undergo unnecessary biopsies.