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In this paper we propose a computer-aided diagnosis system for the breast lesion classification. Our approach is based on quantitative ultrasound and deep learning. We used the Nakagami imaging to create parametric maps of breast lesions that illustrate tissue scattering properties. For this task the sliding window technique was applied. The Nakagami parameter was calculated using the maximum likelihood...
The quantitative ultrasound (QUS) imaging provides additional information on tissue properties in comparison to standard ultrasonography. In this paper we use the Nakagami imaging to address the problem of breast lesion classification. Images of breast lesions may contain areas with calcifications or/and necrosis. These areas are visible on Nakagami maps and their presence make the classification...
The aim of the study was finding the relationship between BIRADS classification combined with envelope K and Nakagami statistics of the echoes backscattered in the breast tissue in vivo and the histological data. 107 breast lesions were examined. Both, the RF echo-signal and B-mode images from the lesions and surrounding tissue were recorded. The analysis method was based on the combining data from...
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