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Ultrasound RF time series for tissue typing: first in vivo clinical results. Tissue differentiation based on radiofrequency echographic signal local spectral content// Proceedings of the IEEE Symposium on Ultrasonics, 2003, 1: 1030-1033. Computer-Aided diagnosis with deep learning architecture: Applications to breast lesions in us images and pulmonary nodules in CT scans. Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis. Bangladesh Journal of Medical Physics, 2013, 4(1): 1-10. Computer-Aided diagnosis of solid breast lesions using an ultrasonic multi-feature analysis procedure. Ultrasound in Medicine & Biology, 2012, 38(7): 1251-1261.Īlam S K, Feleppa E J, Rondeau M, et al. Computer-aided diagnosis based on speckle patterns in ultrasound images. The performance of computer-aided diagnosis for DCIS based on classification of clustered microcalcifications. Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. Automatic detection of abnormal mammograms in mammographic images. An interactive system for computer-aided diagnosis of breast masses. Automated detection of diabetic retinopathy exudates in color fundus images. Segmentation of diabetic macular edema in Oct retinal images. Quantitative Imaging in Medicine and Surgery, 2012, 2(3): 163-176. A review of computer-aided diagnosis in thoracic and colonic imaging. Chinese Journal of Medical Ultrasound ( Electronic Edition), 2011, 8(6): 1227-1233.
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Application value of breast image report data system for ultrasonography in mamary gland. Chinese Journal of Ultrasound in Medicine, 2008, 24(6): 19-23. Discussing of using breast grades in ultrasound.
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Extensive experiments on 928 breast ultrasound RF signals collected from the hospital demonstrate the effectiveness of the new proposed method and its precision, sensitivity, specificity, PPV, NPV and MCC are 89.29%, 75.62%, 94.54%, 97%, 98.3% and 81.01%, respectively.
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At last, we draw on the feature difference between different grades of breast tumors to design a cascade binary tree SVM classifier which not only overcome the problem of sample quantity disequilibrium but also conform to the subjective diagnosis rule of sonographer. First, we utilize the multi-scale geometric characteristic of Shearlet transformation to extract the multi-scale and multi-directional features of ultrasound RF signal, and then reduce the high-dimensional Shearlet features by multi-scale directional binary pattern which can effectively preserve the sufficient discriminated information. A novel efficient method based on the ultrasound radio frequency (RF) signals is proposed to distinguish the breast tumors grades.