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Breast tumor ranks fourth among various cancers in terms of mortality rate in Taiwan, and it is also the most commonly prevalent cancer in females. Early detection of any malignant lesions can increase the survival rate and also decline the mortality rate through current advanced medical therapies. Acoustic radiation force impulse (ARFI) is a new imaging technique for distinguishing breast lesions in the early stage based on localized tissue displacement, which is quantitated by virtual touch tissue imaging (VTI). Digital ARFI-VTI is an initial breast imaging modality and appears to be more effective in women aged >30 years. Therefore, image enhancement process is a key technique to enhance a low-contrast image in a region of interest (ROI) for visualizing texture details and morphological features. In this study, two-dimensional (2D) fractional-order convolution, as a 2D sliding filter window (eight filters are selected), is applied to enhance ARFI-VTI images for an accurate extrapolation of lesions in an ROI. Then, the maximum pooling is performed to reduce the dimensions of the feature patterns from 32×32 to 16×16 size. A multilayer machine vision classifier, as a generalized regression neural network (GRNN), is then used to screen subjects with benign or malignant tumors. With a 10fold cross-validation, promising results such as mean recall (%), mean precision (%), mean accuracy (%), and mean F1 score of 92.92±3.43%, 80.42±6.45%, 87.78±2.17%, and 0.8615±0.0495, respectively, are achieved for quantifying the performance of the proposed classifier. Breast tumors visualized on ARFI-VTI images can be useful as digitalized images for rapid screening of malignant from benign lesions by the proposed machine vision classifier.

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Enhancement of ARFI-VTI Elastography Images in Order to Preliminary Rapid Screening of Benign and Malignant Breast Tumors Using Multilayer Fractional-Order Machine Vision Classifier