Speckle reduction based on fractional-order filtering and boosted singular value shrinkage for optical coherence tomography image. (July 2019)

• Record Type: Journal Article
• Authors: Chen, Huaiguang; Fu, Shujun; Wang, Hong; Wang, Hong; Li, Yuliang; Wang, Fengling
• Is Part Of: Biomedical signal processing and control. Volume 52(2019)
• Page Start: 281
• Page End: 292
• Publication Date: 2019-07
• Subjects: Speckle noise -- Optical coherence tomography -- Fractional-order filtering -- Piecewise Laplace soft shrinkage -- Boosted iterative regularization technique; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.04.033
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A boosted singular value shrinkage algorithm based on fractional-order filtering is proposed. To suppress the influence of noise on similar block matching, the proposed algorithm performs fractional-order pre-filtering on each block. Considering the difference of the effect of noise on singular values, we propose a piecewise Laplace soft threshold shrinkage operator. To avoid the loss of detail information during the iterative process, an improved iterative regularization technique is proposed. Abstract: Optical coherence tomography (OCT) is a micrometer-resolution optical imaging technology that has been widely used in many fields, such as medicine and materials science. However, OCT images inevitably suffer from speckle noise which obscures the structural information of image. To remove speckle noise, a boosted singular value shrinkage algorithm based on fractional-order filtering is proposed in this paper. An OCT image is first divided into many overlapping image blocks and each block is filtered using a fractional mask, and then an absolute distance is used as a similarity criterion for block matching to form a low rank group matrix. Furthermore, the fractional-order preprocessing is performed on the group matrix. Finally, singular value decomposition, a piecewise Laplace shrinkage, aggregating and boosted iterative regularization technique are used to reconstruct a filtered image. Extensive experiments are performed on 18 OCT images of the retina of the human eye to verify the validity of the proposed method. Experimental results show that the proposed method harvests best PSNR, SSIM and EP results in most cases. In addition, results of the paired samples t -test show that the proposed method can remove noise more thoroughly and better preserve the structural information of the OCT images. In summary, the proposed algorithm provides better objective metrics and visual inspection compared with several state-of-the-art denoising algorithms.