Automation of fat infiltrates segmentation from knee muscles

• Main Author: Li, Feng
• Other Authors: Poh, Chueh Loo
• Format: Final Year Project
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Bioengineering
• Online Access: http://hdl.handle.net/10356/61544
• Institution: Nanyang Technological University

This project looks specifically into Sarcopenia, a syndrome characterized by the progressive loss of muscle mass associated with age. Sarcopenia is the central factor in the pathophysiology of frailty. The quantification of muscular fat infiltrates has become increasingly important, as a number of researches have identified a relationship between the accumulation of fat infiltrates and frailty. Therefore, the ability to quantify fats efficiently and accurately is significant for further analysis of this field. Segmenting the fat infiltrates from medical images is a crucial step towards their quantification and visualization. However, there is no previous work performed on semi-automatic or fully automatic segmentation of fat infiltrates. Mostly still depend heavily on manual segmentation that is both tedious and time-consuming. Hence, the aim of this project is to develop an automated segmentation program for the extraction of fat infiltrates from MRI images of the knee muscle. In this project, several procedures for a snake active contour program automated successfully. Most importantly, the choosing of initialization points and the processing of all slices associated with a particular muscle group. In addition, several problems with the accuracy of segmentation were also identified, with the background intensity being the most crucial factor. Deeper analysis was carried out to obtain the optimal factor for segmentation.