Khan, M.U.G., Gotoh, Y. and Nida, N. (2017) Medical image colorization for better visualization and segmentation. In: Valdés Hernández , M. and González-Castro, V., (eds.) Medical Image Understanding and Analysis. Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, 11-13 Jul 2017, Edinburgh. Communications in Computer and Information Science, 723 (723). Springer, Cham , pp. 571-580. ISBN 9783319609638
Abstract
Medical images contain precious anatomical information for clinical procedures. Improved understanding of medical modality may contribute significantly in arena of medical image analysis. This paper investigates enhancement of monochromatic medical modality into colorized images. Improving the contrast of anatomical structures facilitates precise segmentation. The proposed framework starts with pre-processing to remove noise and improve edge information. Then colour information is embedded to each pixel of a subject image. A resulting image has a potential to portray better anatomical information than a conventional monochromatic image. To evaluate the performance of colorized medical modality, the structural similarity index and the peak signal to noise ratio are computed. Supremacy of proposed colorization is validated by segmentation experiments and compared with greyscale monochromatic images.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 Springer International Publishing. This is an author produced version of a paper subsequently published in Communications in Computer and Information Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Medical image enhancement; Colorization; Visualization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Jul 2017 12:21 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://doi.org/10.1007/978-3-319-60964-5_50 |
Status: | Published |
Publisher: | Springer, Cham |
Series Name: | Communications in Computer and Information Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-60964-5_50 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:119356 |