Abstract
This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.
Highlights
Breast cancer in women is an important health problem for both developed and developing countries
This paper demonstrates the suitability of Rician inverse Gaussian (RiIG) distribution [25] for statistical modeling of the Contourlet transformed breast ultrasound images
In this paper, two new approaches in breast tumors classification are presented, employing Rician Inverse Gaussian (RiIG) statistical model-based Weighted Contourlet Parametric images obtained from the Contourlet transformed breast US images
Summary
Breast cancer in women is an important health problem for both developed and developing countries. A recent report by the Cancer Statistics Center of the American Cancer Society shows that among the estimated new cancer cases in 2020, the number of cases of breast cancer is 1,806,590. It shows that around 606,520 cancer deaths are anticipated only in the United States, of which breast cancer contributes to around 279,100 (approximately 46%) [1]. Breast ultrasound (US) imaging is one of the most promising tools to distinguish and classify breast tumors among the other imaging techniques such as mammograms, MRIs, etc. Ultrasonic images are constructed by dispersing pulses of ultrasound into human tissue using a probe. In US imaging, the pulses echo off the body tissues having several reflection properties which are recorded and exhibited as an image. The B-mode or brightness mode image, in turn, shows the acoustic impedance of a cross-section of tissue in two dimensions
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