Please use this identifier to cite or link to this item:
Title: Automation of fat infiltrates segmentation from knee muscles
Authors: Li, Feng
Keywords: DRNTU::Engineering::Bioengineering
Issue Date: 2014
Abstract: 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.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP report-3.pdf
  Restricted Access
9.4 MBAdobe PDFView/Open

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.