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

Ismail Irmakci, Sarfaraz Hussein, Aydogan Savran, Rita R. Kalyani, David Reiter, Chee W. Chia, Kenneth W. Fishbein, Richard G. Spencer, Luigi Ferrucci, Ulas Bagci*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft-tissue contrast and lack of ionizing radiation. However, quantification of body composition requires an accurate segmentation of fat, muscle, and other tissues from MR images, which remains a challenging goal due to the intensity overlap between them. In this study, we propose a fully automated, data-driven image segmentation platform that addresses multiple difficulties in segmenting MR images such as varying inhomogeneity, non-standardness, and noise, while producing a high-quality definition of different tissues. In contrast to most approaches in the literature, we perform segmentation operation by combining three different MRI contrasts and a novel segmentation tool, which takes into account variability in the data. The proposed system, based on a novel affinity definition within the fuzzy connectivity image segmentation family, prevents the need for user intervention and reparametrization of the segmentation algorithms. In order to make the whole system fully automated, we adapt an affinity propagation clustering algorithm to roughly identify tissue regions and image background. We perform a thorough evaluation of the proposed algorithm's individual steps as well as comparison with several approaches from the literature for the main application of muscle/fat separation. Furthermore, whole-body tissue composition and brain tissue delineation were conducted to show the generalization ability of the proposed system. This new automated platform outperforms other state-of-the-art segmentation approaches both in accuracy and efficiency.

Original languageEnglish (US)
Article number8451946
Pages (from-to)1069-1081
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Issue number4
StatePublished - Apr 2019
Externally publishedYes


  • MRI
  • affinity propagation
  • brain tissue segmentation
  • fat quantification
  • fat segmentation
  • muscle quantification
  • muscle segmentation
  • whole-body tissue classification

ASJC Scopus subject areas

  • Biomedical Engineering


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