Medical image denoising based on sparse dictionary learning and cluster ensemble
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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.
KeywordsMedical image denoising Self-similarity Cluster ensemble Sparse dictionary learning
The authors would like to thank all those who have helped us during the writing of the paper and the anonymous reviewers for their constructive comments. This work was funded by the International Postdoctoral Exchange Fellowship Program 2013 by the Office of China Postdoctoral Council (No. 20130026), the China Postdoctoral Science Foundation Special funded project (No. 2012T50799) and the Open Research Fund of Key Laboratory of Spectral Imaging Technology by Chinese Academy of Sciences.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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