Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/61099
Title: Medical image analysis of knee images for sarcopenia detection using mri
Authors: Chitradevi
Keywords: DRNTU::Engineering
Issue Date: 2014
Abstract: Osteoarthritis is a degenerative syndrome with health impairment leads to decrease in their physical activities, inflammation, stiffness and severe pain in the elderly population. It is necessary for us to know about the mechanism behind Progressive loss of cartilage for the efficient diagnosis and treatment of different stages of osteoarthritis. Now the fast world is expecting an easy and non-invasive technique for diagnosing the disease faster with at most accuracy. So we are going for MRI, which is the most prominent widely used tool to access intra articular surfaces, cartilage in particular. The purpose of calculating the thickness of cartilage is to understand the progressive loss of cartilage and to study the clinical interventions. The aim of the project is to extract and analyze the femoral articular cartilage of the knee joint through medical image analysis. An MRI image was segmented using a number of software, including Matlab and volumetric analysis was performed using the results obtained from these images. We performed manual segmentation, providing anatomical information; in detecting the physical changes in their cartilage structure. Through this we facilitate the clinician to visualize the large amount of data of a particular patient within limited time. Semi–Automatic segmentation include thresholding which would be partially solved by deformable methods, while extracting the cartilage tissue there will be biggest variation in every OA subjects with cartilage in homogeneities and with minimum inter-tissue contrast. Our result helps to analyze the anatomical knee images of five patients and to visualize the thickness of cartilage more rapidly.
URI: http://hdl.handle.net/10356/61099
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Theses

Files in This Item:
File Description SizeFormat 
Tamilselvan14.pdf
  Restricted Access
Main thesis1.85 MBAdobe PDFView/Open

Page view(s)

195
Updated on Apr 15, 2021

Download(s)

8
Updated on Apr 15, 2021

Google ScholarTM

Check

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