Masters Theses
Abstract
"In this research, imaging techniques are investigated for the analysis and detection of abnormalities in cervical and lumbar vertebrae. Detecting vertebra anomalies pertaining to osteoarthritis such as claw, traction and anterior osteophytes can aide in treatment plans for the patient. New size invariant features were developed for the detection of claw, traction and anterior osteophytes in cervical spine vertebrae. Using a K-means clustering and nearest centroid classification approach, the results were generated that were capable of discriminating cervical vertebrae for presence of anomalies related to osteophytes. The techniques developed can be integrated into systems based on querying spine images to be classified for such anomalies. Computed tomography (CT) scan images of lumbar spine models are investigated and three dimensional models are generated for studying the shape and structure of the lumbar spine. Using the 3D models, techniques are developed for the detection of traction in lumbar x-ray images. Using K-means clustering and nearest centroid classification, attempts are made to classify lumbar spine images based on presence of traction"--Abstract, page iii.
Advisor(s)
Erçal, Fikret
Stanley, R. Joe
Committee Member(s)
McMillin, Bruce M.
Moss, Randy Hays, 1953-
Department(s)
Computer Science
Degree Name
M.S. in Computer Science
Sponsor(s)
National Library of Medicine (U.S.)
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2008
Pagination
ix, 89 pages
Note about bibliography
Includes bibliographical references (pages 112-115).
Rights
© 2008 Mohammed Das, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Cervical vertebrae -- Abnormalities -- Diagnosis
Image processing -- Digital techniques
Spine -- Radiography
Vertebrae -- Abnormalities -- Diagnosis
Thesis Number
T 9341
Print OCLC #
260030709
Electronic OCLC #
213813242
Link to Catalog Record
Recommended Citation
Das, Mohammed, "Image analysis techniques for vertebra anomaly detection in X-ray images" (2008). Masters Theses. 6854.
https://scholarsmine.mst.edu/masters_theses/6854