Journal of Imaging, Volume 7, Issue 1
January 2021 - 11 articles
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Cover Story: An unsupervised machine learning technique is presented that is reinforced with hypothesis testing and statistical inference to iteratively segment the reconstructed image of a breast into fat, transition, fibroglandular, and malignant tissues. This segmentation leads to decomposition of the breast interior into disjoint tissue masks. An array of metrics is applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed, and are supplemented with qualitative information. View this paper 
