Feature-oriented singular value shrinkage for optical coherence tomography image. (March 2019)

• Record Type: Journal Article
• Authors: Chen, Huaiguang; Fu, Shujun; Wang, Hong; Lv, Hongli; Zhang, Caiming; Wang, Fengling; Li, Yuliang
• Is Part Of: Optics and lasers in engineering. Volume 114(2019)
• Page Start: 111
• Page End: 120
• Publication Date: 2019-03
• Subjects: Optical coherence tomography -- Speckle -- Singular value shrinkage -- Mixed threshold -- Adaptive backward projection -- Low-rank; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2018.10.019
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A singular value shrinkage denoising algorithm in low-rank approximation framework. Block matching with the weighted absolute distance measurement. Singular values are shrunk with mixed thresholding. Adaptive backward projection based on image features. Abstract: Optical coherence tomography (OCT) is a non-invasive optical imaging modality that has been widely used in the field of medical diagnosis. However, OCT images are often degraded by speckle noise. To address this problem, this paper proposes a two-stage feature-oriented singular value shrinkage algorithm in a low-rank approximation framework, for speckle noise reduction and contrast enhancement of intra-retinal layers of OCT images. First, a weighted absolute distance is employed to find nonlocal similar patches that exhibit high correlation to a given reference one. Next, the singular values of the group matrix formed by similar patches are shrunk by mixed thresholding so that they are closer to the singular values of that of latent noise-free image. Finally, an iterative regularization technique is adopted to improve the denoising performance of the proposed method, where the backward projection parameter in each pixel is adaptively determined by its corresponding gradient variation. By using these strategies, the proposed method not only effectively removes the speckle noise of OCT images, but also preserves fine structural information of objects. Experimental results show that the proposed algorithm is competitive with some state-of-the-art speckle removal techniques in terms of both objective metrics and subjective visual inspection.