Members: Azubuike Okorie, Sokratis Makrogiannis
Tissue Identification and Quantification techniques for Body Composition Imaging consitute a broad spectrum of research that focuses on segmentation of tibial lengths and analysis of those tibial sites using peripheral Quantitative Computed Tomography (pQCT). The tibial lengths at 4%, 38%, and 66% are manually segmented to provide reference masks using National Institutes of Health (NIH) software named MIPAV (Medical Image Processing, Analysis and Visualization). Also, a software model called TIDAQ (Tissue Identification Analysis and Quantification) created by our MIVIC lab head Dr. Makrogiannis, is applied to the same lower leg scans to provide an automated segmentation. A validation analysis is performed between the manual segmentation and automated segmentation. Additionally, thigh tissue identification and quantification techniques wer applied to Computed Tomography (CT) imaging. The goal is to evaluate the level of agreement of results between supervised quantification methods that and our results.
Various methods and techniques are used for identification and quantification of tissue types. In the process of validating the quantification and segmentation of mask produced by TIDAQ, a comparison is made to a world standard known as the Bonalyse. In the quantification of scans using Bonalyse method, pixel determination of hard tissue is used. The cortical bone is determined as being greater than 710 mg cm^-3 of mineral density while the trabecular is figured to be in between 180 and 71 mg cm^-3. During the segmentation of 4% scans the trabecular tibia is segmented, while both the cortical and trabecular tibia are segmented on the 38% scans. Finally, fat, muscle, cortical and trabecular bone are segmented at 66% tibial length.
- , “Multi-atlas-based tissue identification in the lower leg using pQCT”, in SPIE Medical Imaging 2020: Image Processing, 2020.
- , “Learning Graph Cut Class Prototypes for Thigh CT Tissue Identification”, in Advances in Visual Computing, Cham, 2019.
- , “Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography”, Physiological Measurement, vol. 39, p. 035011, 2018.
- , “Software system for computing material and structural properties of bone and muscle in the lower extremity from pQCT”, Proc. SPIE 9112, Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring IV, vol. 9112. pp. 911216-911216-6, 2014.