Medical image modality classification using feature weighted clustering approach
| dc.contributor.author | Cchandra, Bhavik Anil | |
| dc.date.accessioned | 2015-05-21T07:55:52Z | |
| dc.date.available | 2015-05-21T07:55:52Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | Medical Image Retrieval System is an area of great importance to the healthcare providers. With a variety of modalities in use, it is important that retrieval systems can provide filtering of results based on modality. The current method of identifying modalities is not accurate and often wrong modality is assigned to the medical images. The objective of this work was to propose a methodology to classify existing medical images by inspecting their visual features and reducing human intervention. This thesis proposes the clustering of medical images using MPEG 7 Visual Descriptor, SCAD clustering with simultaneous feature discrimination and using k-Nearest Neighbour classification. Using this proposed work, the features that are relevant to the cluster are assigned higher weights, giving importance to a number of features that represent the clusters. The features that are not significant to the cluster are not totally ignored instead given lower weights. Using this method, the medical images will be clustered and categorized into their respective modality based on their visual features. In this thesis, emphasis is given to Edge Histogram Descriptor, Color Layout Descriptor and Homogeneous Texture Descriptor of MPEG-7. The proposed method is tested against Fuzzy C-Mean clustering. This thesis also concludes that medical image modality classification is possible depending on the strength of the feature descriptors to detect the characteristics of the medical images. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/657 | |
| dc.language.iso | en | en_US |
| dc.title | Medical image modality classification using feature weighted clustering approach | en_US |
| dc.type | Thesis | en_US |
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