Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/8568
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSoundalgekar, P.-
dc.contributor.authorKulkarni, M.-
dc.contributor.authorNagaraju, D.-
dc.contributor.authorSowmya, Kamath S.-
dc.date.accessioned2020-03-30T10:22:26Z-
dc.date.available2020-03-30T10:22:26Z-
dc.date.issued2018-
dc.identifier.citationProceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018, 2018, Vol.2018-January, , pp.369-373en_US
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/8568-
dc.description.abstractMedical image retrieval (MedIR) is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content. Traditional models do not take the intrinsic characteristics of data into consideration and have achieved limited accuracy in application to medical images. Manifold Ranking (MR) is a technique that can be used in further optimizing precision and recall in MedIR applications as it ranks items by traversing a dynamically constructed content-specific information graph. In this paper, a MedIR approach based on Manifold Ranking is proposed. Medical images being multi-dimensional, exhibit underlying cluster and manifold information which enhances semantic relevance and allows for label uniformity. Hence, when adapted for MedIR, MR can help in achieving large-scale ranking across datasets as is the case in most medical imaging applications. In addition, a relevance feedback mechanism was also incorporated to support a learning based system. We show that MR achieved significant improvement in retrieval results with relevance feedback as compared to the Euclidean Distance (ED) rankings. This showcases the importance of analyzing the inherent latent structure in medical image data for better performance over traditional methods. � 2018 IEEE.en_US
dc.titleMedical Image Retrieval Using Manifold Ranking with Relevance Feedbacken_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
File Description SizeFormat 
13 Medical Image Retrieval.pdf315.98 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.