A shape-based approach for image retrieval in healthcare information infrastructure

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dc.contributor.advisorAdigun, M.O.
dc.contributor.authorOlugbara, Oludayo Olufolorunsho
dc.date.accessioned2012-12-12T07:29:26Z
dc.date.available2012-12-12T07:29:26Z
dc.date.issued2008
dc.descriptionSubmitted to the Faculty of Science in the Department of Computer Science at the University of Zululand, 2008.en_US
dc.description.abstractThis study investigated some models and techniques that would help build shape-based image retrieval with an improved accuracy. As an initial step, a modular prototype system, called BrainSearch was implemented and used to demonstrate the utility of our algorithms and techniques on brain Magnetic Resonance Imaging (MR1) characterization and their suitability for image retrieval. The system supports retrieval based on shape similarity, a single keyword image annotation and five brain MRl classes. The BrainSearch system was realized to make it easy to test retrieval performance and to expedite further algorithm investigation. This was made possible by the implementation of region-based Local Binary Fitting (LBF) active contour, Density histogram of Feature Points (DFP) shape representation and k-Nearest Neighbor (k-NN) classifier. Then we performed a series of experiments to evaluate the performance of BrainSearch utilizing different retrieval techniques. Results generally showed that (a) region-based DFP shape representation is better than edge-based DFP shape representation, whether pre-classification of images is used or not, (b) retrieval technique that uses pre-classification of images gives better results than retrieval technique that uses non-classification of images, no matter the DFP shape representation used, (c) the pre-classification of images cannot improve edge-based DFP shape representation better than when region based DFP alone is used, and (d) the pre-classification of images as well as factors, like shape representations and similarity measures, improve retrieval performance of BrainSearch system. Overall, the hybrid combination of LBF active contour, DFP shape representation and k-NN classifier is promising for the retrieval ofbrain MRI.en_US
dc.description.sponsorshipNational Research Fund (NRF), Center of Excellence for mobile e-services and IFIB WGen_US
dc.identifier.urihttps://hdl.handle.net/10530/1121
dc.language.isoenen_US
dc.publisherUniversity of Zululanden_US
dc.subjectImage retrievalen_US
dc.subjectShape-based image retrievalen_US
dc.titleA shape-based approach for image retrieval in healthcare information infrastructureen_US
dc.typeThesisen_US
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