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Graph-based rate control in pathology imaging with lossless region of interest coding
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Sanchez Silva, Victor and Hernández-Cabronero, Miguel (2018) Graph-based rate control in pathology imaging with lossless region of interest coding. IEEE Transactions on Medical Imaging, 37 (10). pp. 2211-2223. doi:10.1109/TMI.2018.2824819 ISSN 1558-254X.
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Official URL: http://dx.doi.org/10.1109/TMI.2018.2824819
Abstract
The increasing availability of digital pathology images has motivated the design of tools to foster multidisciplinary collaboration among researchers, pathologists, and computer scientists. Telepathology plays an important role in the development of collaborative tools, as it facilities the transmission and access of pathology images by multiple users. However, the huge ?le size associated with pathology images usually prevents full exploitation of the collaborative telepathology system potential. Within this context, rate control (RC) is an important tool that allows meeting storage and bandwidth requirements by controlling the bit rate of the coded image. In this paper, we propose a novel graph-based RC algorithm with lossless region of interest (RoI) coding for pathology images. The algorithm, which is designed for block-based predictive transform coding methods, compresses the non-RoI in a lossy manner according to a target bit rate and the RoI in a lossless manner. It employs a graph where each node represents a constituent block of the image to be coded. By incorporating information about the coding cost similarities of blocks into the graph, a graph kernel is used to distribute a target bit budget among the non-RoI blocks. In order to increase RC accuracy, the algorithm uses a rate-lambda (R-λ) model to approximate the slope of the rate-distortion curve of the non-RoI, and a graph-based approach to guarantee that the target bit rate is accurately attained. The algorithm is implemented in the HEVC standard and tested over a wide range of pathology images with multiple RoIs. Evaluation results show that it outperforms other state-of-the-art methods designed for single images by very accurately attaining the target bit rate of the non-RoI.
Item Type: | Journal Article | |||||||||
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Subjects: | R Medicine > RB Pathology R Medicine > RC Internal medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Pathology, Diagnostic imaging, Medical technology | |||||||||
Journal or Publication Title: | IEEE Transactions on Medical Imaging | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 1558-254X | |||||||||
Official Date: | October 2018 | |||||||||
Dates: |
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Volume: | 37 | |||||||||
Number: | 10 | |||||||||
Page Range: | pp. 2211-2223 | |||||||||
DOI: | 10.1109/TMI.2018.2824819 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 21 May 2018 | |||||||||
Date of first compliant Open Access: | 21 May 2018 | |||||||||
RIOXX Funder/Project Grant: |
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