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dc.contributor.authorKarthik, K.-
dc.contributor.authorSowmya, Kamath S.-
dc.date.accessioned2020-03-30T09:59:17Z-
dc.date.available2020-03-30T09:59:17Z-
dc.date.issued2018-
dc.identifier.citation2018 13th International Conference on Industrial and Information Systems, ICIIS 2018 - Proceedings, 2018, Vol., , pp.7-12en_US
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/7504-
dc.description.abstractWith the proliferation of various imaging based diagnostic procedures in the healthcare field, patient-specific scan images constitute huge volumes of data that needs to be well-organized and managed for supporting clinical decision support applications. One such crucial application with a significant impact on point-of-care treatment quality is a Content Based Medical Image Retrieval (CBMIR) system that can assist doctors in disease diagnosis based on similar image retrieval. Medical images are multi-dimensional and often contain manifold information, due to which efficient techniques for optimal feature extraction from large-scale image collections are the need of the day. In this paper, an efficient CBMIR model is proposed that is built on multi-level feature sets extracted from medical images. Four different feature extraction techniques are used to optimally represent images in a multi-dimensional feature space, for facilitating classification using supervised machine learning algorithms and top-k similar image retrieval. Experimental validation of proposed model on the standard ImageCLEF 2009 dataset containing 12,560 X-ray images across 116 classes showed promising results in terms of classification accuracy of 85.91%. � 2018 IEEE.en_US
dc.titleA Hybrid Feature Modeling Approach for Content-Based Medical Image Retrievalen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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