"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.
Stanley, R. Joe
McMillin, Bruce M.
Moss, Randy Hays, 1953-
M.S. in Computer Science
National Library of Medicine (U.S.)
Missouri University of Science and Technology
ix, 89 pages
© 2008 Mohammed Das, All rights reserved.
Thesis - Open Access
Library of Congress Subject Headings
Cervical vertebrae -- Abnormalities -- Diagnosis
Image processing -- Digital techniques
Spine -- Radiography
Vertebrae -- Abnormalities -- Diagnosis
Print OCLC #
Electronic OCLC #
Link to Catalog Record
Das, Mohammed, "Image analysis techniques for vertebra anomaly detection in X-ray images" (2008). Masters Theses. 6854.