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Medical & Biological Engineering & Computing

Erschienen in:

06.11.2017 | Original Article

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction

verfasst von: Xiaoying Song, Qijun Huang, Sheng Chang, Jin He, Hao Wang

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 6/2018

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Abstract

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively.

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Titel: Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction
verfasst von: Xiaoying Song
Qijun Huang
Sheng Chang
Jin He
Hao Wang
Publikationsdatum: 06.11.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 6/2018
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-017-1741-8

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