Medical image processing and analysis of MRI images for sarcopenia detection I

• Main Author: Hotchandani, Ruchi
• Other Authors: Poh Chueh Loo
• Format: Final Year Project
• Language: English
• Published: 2015
• Subjects: DRNTU::Science::Medicine::Biomedical engineering
• Online Access: http://hdl.handle.net/10356/65082
• 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 studies 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, while there exists a program for the semi-automatic segmentation of fat infiltrates, its accuracy is limited. Therefore, it increases the dependency on manual segmentation which is both tedious and time-consuming. Hence, the aim of this project is to improve the efficiency and accuracy of the existing semi-automated segmentation program for the extraction of fat infiltrates from MRI images of the knee muscle. In this project, several procedures for the improvement of snake active contour program were implemented successfully:  the algorithm for choosing of initialization points was modified to increase the efficiency and accuracy of selection  the number of iterations for each muscle group was re-evaluated and changed to obtain more accurate results iii  different image processing methods were tested to improve the quality of MRI images, making it easier for the program to identify fat infiltrates. In addition, other problems with the accuracy of segmentation were also identified, with the contrast between muscle and fat being the most crucial factor. Several tests and deeper analysis was carried out to obtain more accurate results.