Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The pr...

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Bibliographic Details
Published in:Journal of digital imaging Vol. 29; no. 6; pp. 706 - 715
Main Authors: Song, Xiaoying, Huang, Qijun, Chang, Sheng, He, Jin, Wang, Hao
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2016
Springer Nature B.V
Subjects:
Adaptive algorithms
Algorithms
Diagnostic systems
Image compression
Image quality
Imaging
Lossless
Medical imaging
Medicine
Medicine & Public Health
Radiology
Block searching
Spatial prediction
Near-lossless compression
Adaptive block size
Lossless Hadamard transform
ISSN:0897-1889, 1618-727X, 1618-727X
Online Access:Get full text
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Summary:To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.
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ISSN:0897-1889
1618-727X
1618-727X
DOI:10.1007/s10278-016-9892-y