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Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variance-based local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MOD-LTP and iteratively reweighting the moment features of MOD-LTP based on the user's feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images.IEEE Trans. Inf. Technol. Biomed., 14, 897903.) in retrieving the first 10 relevant images. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Computer Journal Oxford University Press

Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

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References (22)

Publisher
Oxford University Press
Copyright
The British Computer Society 2014. All rights reserved. For Permissions, please email: journals.permissionsoup.com
ISSN
0010-4620
eISSN
1460-2067
DOI
10.1093/comjnl/bxu008
Publisher site
See Article on Publisher Site

Abstract

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variance-based local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MOD-LTP and iteratively reweighting the moment features of MOD-LTP based on the user's feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images.IEEE Trans. Inf. Technol. Biomed., 14, 897903.) in retrieving the first 10 relevant images.

Journal

The Computer Journal – Oxford University Press

Published: Sep 25, 2014

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