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Magnetic resonance imaging (MRI) is the non-invasive modality of choice forbody tissue composition analysis due to its excellent soft tissue contrast andlack of ionizing radiation. However, quantification of body compositionrequires an accurate segmentation of fat, muscle and other tissues from MRimages, which remains a challenging goal due to the intensity overlap betweenthem. In this study, we propose a fully automated, data-driven imagesegmentation platform that addresses multiple difficulties in segmenting MRimages such as varying inhomogeneity, non-standardness, and noise, whileproducing high-quality definition of different tissues. In contrast to mostapproaches in the literature, we perform segmentation operation by combiningthree different MRI contrasts and a novel segmentation tool which takes intoaccount variability in the data. The proposed system, based on a novel affinitydefinition within the fuzzy connectivity (FC) image segmentation family,prevents the need for user intervention and reparametrization of thesegmentation algorithms. In order to make the whole system fully automated, weadapt an affinity propagation clustering algorithm to roughly identify tissueregions and image background. We perform a thorough evaluation of the proposedalgorithm's individual steps as well as comparison with several approaches fromthe literature for the main application of muscle/fat separation. Furthermore,whole-body tissue composition and brain tissue delineation were conducted toshow the generalization ability of the proposed system. This new automatedplatform outperforms other state-of-the-art segmentation approaches both inaccuracy and efficiency.

A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation