Masters Theses

Mohammed Das

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

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