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| Title: | Vertebra shape classification using MLP for content-based image retrieval | |
| Author (s): | Antani, A. Long, L.R. Thoma, G.R. Stanley, R. Joe | |
| Department/Lab Affiliations: | Electrical and Computer Engineering Image Processing Laboratory | |
| Keywords: | 704 cervical spine vertebrae Lister Hill National Center for Biomedical Communications MLP National Library for Medicine anterior osteophytes anterior portion automatically classified pathology automatically detected pathology bone cervical digitized X-ray content-based image retrieval content-based retrieval curvature analysis diagnostic radiography image classification image features extraction image retrieval information retrieval systems lumbar spine digitized X-ray medical image processing morphological analysis multilayer perceptron multilayer perceptrons multimedia information retrieval system neural networks protrusion regions quantification second national health and nutrition examination survey semantic retrieval vertebra boundary vertebra shape classification | |
| Issue Date: | 2003 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Antani, A.; Long, L.R.; Thoma, G.R.; Stanley, R.J., "Vertebra shape classification using MLP for content-based image retrieval" Proceedings of the International Joint Conference on Neural Networks, 2003. pp. 160- 165 vol.1, 20-24 July 2003 | |
| Abstract: | A desirable content-based image retrieval (CBIR) system would classify extracted image features to support some form of semantic retrieval. The Lister Hill National Center for Biomedical Communications, an intramural R&D division of the National Library for Medicine (NLM), maintains an archive of digitized X-rays of the cervical and lumbar spine taken as part of the second national health and nutrition examination survey (NHANES II). It is our goal to provide shape-based access to digitized X-rays including retrieval on automatically detected and classified pathology, e.g., anterior osteophytes. This is done using radius of curvature analysis along the anterior portion, and morphological analysis for quantifying protrusion regions along the vertebra boundary. Experimental results are presented for the classification of 704 cervical spine vertebrae by evaluating the features using a multi-layer perceptron (MLP) based approach. In this paper, we describe the design and current status of the content-based image retrieval (CBIR) system and the role of neural networks in the design of an effective multimedia information retrieval system. | |
| Type: | Article - Conference proceedings text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Vertebra shape classification using MLP for content-based image retrieval | |
| contributor.author | Antani, A. | |
| contributor.author | Long, L.R. | |
| contributor.author | Thoma, G.R. | |
| contributor.author | Stanley, R. Joe | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Image Processing Laboratory | |
| subject | 704 cervical spine vertebrae | |
| subject | Lister Hill National Center for Biomedical Communications | |
| subject | MLP | |
| subject | National Library for Medicine | |
| subject | anterior osteophytes | |
| subject | anterior portion | |
| subject | automatically classified pathology | |
| subject | automatically detected pathology | |
| subject | bone | |
| subject | cervical digitized X-ray | |
| subject | content-based image retrieval | |
| subject | content-based retrieval | |
| subject | curvature analysis | |
| subject | diagnostic radiography | |
| subject | image classification | |
| subject | image features extraction | |
| subject | image retrieval | |
| subject | information retrieval systems | |
| subject | lumbar spine digitized X-ray | |
| subject | medical image processing | |
| subject | morphological analysis | |
| subject | multilayer perceptron | |
| subject | multilayer perceptrons | |
| subject | multimedia information retrieval system | |
| subject | neural networks | |
| subject | protrusion regions quantification | |
| subject | second national health and nutrition examination survey | |
| subject | semantic retrieval | |
| subject | vertebra boundary | |
| subject | vertebra shape classification | |
| date.issued | 2003 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Antani, A.; Long, L.R.; Thoma, G.R.; Stanley, R.J., "Vertebra shape classification using MLP for content-based image retrieval" Proceedings of the International Joint Conference on Neural Networks, 2003. pp. 160- 165 vol.1, 20-24 July 2003 | |
| identifier.issn | 1098-7576 | |
| identifier.pub.URI | ||
| description.abstract | A desirable content-based image retrieval (CBIR) system would classify extracted image features to support some form of semantic retrieval. The Lister Hill National Center for Biomedical Communications, an intramural R&D division of the National Library for Medicine (NLM), maintains an archive of digitized X-rays of the cervical and lumbar spine taken as part of the second national health and nutrition examination survey (NHANES II). It is our goal to provide shape-based access to digitized X-rays including retrieval on automatically detected and classified pathology, e.g., anterior osteophytes. This is done using radius of curvature analysis along the anterior portion, and morphological analysis for quantifying protrusion regions along the vertebra boundary. Experimental results are presented for the classification of 704 cervical spine vertebrae by evaluating the features using a multi-layer perceptron (MLP) based approach. In this paper, we describe the design and current status of the content-based image retrieval (CBIR) system and the role of neural networks in the design of an effective multimedia information retrieval system. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:17:08Z | |
| date.available | 2007-04-05T14:17:08Z | |
| identifier.persist.URI | ||
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