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An Efficient Radiographic Image Retrieval System Using Convolutional Neural Network
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-7767-3399
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0001-5765-2964
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2016 (English)In: 2016 23rd International Conference on Pattern Recognition (ICPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3134-3139, article id 7900116Conference paper, Published paper (Refereed)
Abstract [en]

Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high- level image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the “Image Retrieval in Medical Applications” (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 3134-3139, article id 7900116
Series
Proceedings - International Conference on Pattern Recognition, ISSN 1051-4651
Keywords [en]
Content based image retrieval and data mining, Medical image and signal analysis, Deep learning
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-197570DOI: 10.1109/ICPR.2016.7900116ISI: 000406771303020Scopus ID: 2-s2.0-85019074329ISBN: 9781509048472 (print)OAI: oai:DiVA.org:kth-197570DiVA, id: diva2:1052120
Conference
23rd International Conference on Pattern Recognition, ICPR 2016, Cancun CenterCancun, Mexico, 4 December 2016 through 8 December 2016
Funder
Swedish Research Council, 2012-3512Swedish Research Council, 2014-6153VINNOVA, E9126
Note

QC 20161208

Available from: 2016-12-05 Created: 2016-12-05 Last updated: 2017-10-10Bibliographically approved

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