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Soft Computing

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23-10-2017 | Focus

Medical image denoising based on sparse dictionary learning and cluster ensemble

Authors: Jing Bai, Shu Song, Ting Fan, Licheng Jiao

Published in: Soft Computing | Issue 5/2018

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Abstract

Medical imaging techniques play a very important role in modern life. However, due to the technique limitation, the random noise often degrades the quality of acquired medical images, which seriously affects the medical image analysis. A denoising scheme that combines sparse dictionary learning with cluster ensemble is proposed in our paper, which exploits both the inherent self-similarity in images and sparsity of image patches. Firstly, construct image feature set by using steering kernel regression. Then, the effective cluster ensemble method is utilized to gain the class label of image feature set. Finally, for each cluster, an adaptive dictionary was trained by the sparse dictionary learning algorithm. The trained dictionary is more adaptive and stable, which is beneficial to improve the quality of recovered image. The experiment validates the superiorities of our proposed method and has a satisfactory speed.

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Title: Medical image denoising based on sparse dictionary learning and cluster ensemble
Authors: Jing Bai
Shu Song
Ting Fan
Licheng Jiao
Publication date: 23-10-2017
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 5/2018
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2853-7

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