Dildar Hussain1, Rizwan Ali Naqvi2, Woong-Kee Loh3, Jooyoung Lee1,*
CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2587-2598, 2021, DOI:10.32604/cmc.2021.013031
Abstract Many existing techniques to acquire dual-energy X-ray absorptiometry (DXA) images are unable to accurately distinguish between bone and soft tissue. For the most part, this failure stems from bone shape variability, noise and low contrast in DXA images, inconsistent X-ray beam penetration producing shadowing effects, and person-to-person variations. This work explores the feasibility of using state-of-the-art deep learning semantic segmentation models, fully convolutional networks (FCNs), SegNet, and U-Net to distinguish femur bone from soft tissue. We investigated the performance of deep learning algorithms with reference to some of our previously applied conventional image segmentation techniques (i.e., a decision-tree-based method using… More >
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