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| Title: | Image analysis techniques for vertebra anomaly detection in X-ray images | |
| Author (s): | Das, Mohammed (Mohammed Sadiq), 1985- | |
| Advisor(s): | Ercal, Fikret Stanley, R. Joe | |
| Issue Date: | 2008 | |
| Publisher: | University of Missouri--Rolla i.e.[Missouri University of Science and Technology] | |
| Citation: | Das, Mohammed. "Image analysis Techniques for Vertebra Anomaly Detection in X-Ray Images." Master's Thesis, Computer Science, University of Missouri-Rolla, 2008. | |
| 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, leaf iii. | |
| Type: | Thesis/Dissertation text | |
| Copyright Notice: | These materials are protected under copyright by the original author. | |
| Link to this page: | ||
| Full Text: |
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| title | Image analysis techniques for vertebra anomaly detection in X-ray images | |
| contributor.advisor | Ercal, Fikret | |
| contributor.advisor | Stanley, R. Joe | |
| contributor.author | Das, Mohammed (Mohammed Sadiq), 1985- | |
| contributor.sponsor | National Library of Medicine | |
| subject.LCSH | Cervical vertebrae -- Abnormalities -- Diagnosis. | |
| subject.LCSH | Image processing -- Digital techniques. | |
| subject.LCSH | Spine -- Radiography. | |
| subject.LCSH | Vertebrae -- Abnormalities -- Diagnosis. | |
| date.issued | 2008 | |
| publisher | University of Missouri--Rolla i.e.[Missouri University of Science and Technology] | |
| identifier.citation | Das, Mohammed. "Image analysis Techniques for Vertebra Anomaly Detection in X-Ray Images." Master's Thesis, Computer Science, University of Missouri-Rolla, 2008. | |
| identifier.oclc | 213813242 | |
| identifier.pub.URI | ||
| description | Degree granted by Missouri University of Science and Technology, formerly known as University of Missouri--Rolla. | |
| description | Includes bibliographical references (p. 87-88). | |
| description | Mode of access: World Wide Web. | |
| description | System requirements: Adobe Acrobat Reader; Internet browser. | |
| description | The entire thesis text is included in file. | |
| description | Thesis (M.S.)--Missouri University of Science and Technology, 2008. | |
| description | Title from title screen of thesis/dissertation PDF file (viewed March 24, 2008) | |
| description | Vita. | |
| description.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, leaf iii. | |
| description. statementOfResponsibility | by Mohammed Das. | |
| type | Thesis/Dissertation | |
| type.DCMIType | text | |
| rights | These materials are protected under copyright by the original author. | |
| language.ISO639-2 | eng | |
| format.extent | ix, 89 p. : ill., digital, PDF file. | |
| date.accessioned | 2008-03-24T17:07:38Z | |
| date.available | 2008-03-24T17:07:41Z | |
| identifier.persist.URI | ||
| Full Text |
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