File(s) under permanent embargo
A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval
journal contribution
posted on 2018-06-15, 00:00 authored by Seyedamin Khatami, M Babaie, H R Tizhoosh, Abbas KhosraviAbbas Khosravi, Thanh Thi NguyenThanh Thi Nguyen, Saeid NahavandiClosing the semantic gap in medical image analysis is critical. Access to large-scale datasets might help to narrow the gap. However, large and balanced datasets may not always be available. On the other side, retrieving similar images from an archive is a valuable task to facilitate better diagnosis. In this work, we concentrate on forming a search space, consisting of the most similar images for a given query, to be used for a similarity-based search technique in a retrieval system. We propose a two-step hierarchical shrinking search space when local binary patterns are used. Transfer learning via convolutional neural networks is utilized for the first stage of search space shrinking, followed by creating a selection pool using Radon transform for further reduction. The difference between two orthogonal Radon projections is considered in the selection pool to extract more information. The IRMA dataset, from ImageCLEF initiative, containing 14,400 X-ray images, is used to validate the proposed scheme. We report a total IRMA error of 168.05 (or 90.30% accuracy) which is the best result compared with existing methods in the literature for this dataset when real-time processing is considered.
History
Journal
Expert systems with applicationsVolume
100Pagination
224 - 233Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2018, ElsevierUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC