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Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images

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Abstract

Image denoising is a fundamental preprocessing step of image processing in many applications developed for optical coherence tomography (OCT) retinal imaging—a high-resolution modality for evaluating disease in the eye. To make a homogeneity similarity-based image denoising method more suitable for OCT image removal, we improve it by considering the noise and retinal characteristics of OCT images in two respects: (1) median filtering preprocessing is used to make the noise distribution of OCT images more suitable for patch-based methods; (2) a rectangle neighborhood and region restriction are adopted to accommodate the horizontal stretching of retinal structures when observed in OCT images. As a performance measurement of the proposed technique, we tested the method on real and synthetic noisy retinal OCT images and compared the results with other well-known spatial denoising methods, including bilateral filtering, five partial differential equation (PDE)-based methods, and three patch-based methods. Our results indicate that our proposed method seems suitable for retinal OCT imaging denoising, and that, in general, patch-based methods can achieve better visual denoising results than point-based methods in this type of imaging, because the image patch can better represent the structured information in the images than a single pixel. However, the time complexity of the patch-based methods is substantially higher than that of the others.

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Acknowledgments

This work was supported by a grant from the Fundamental Research Funds for the Central Universities, grant no. 30920140111004, the Qing Lan Project, and the Bio-X Interdisciplinary Initiatives Program of Stanford University.

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Authors and Affiliations

  1. Department of Radiology, Stanford University, Stanford, CA, 94305, USA

    Qiang Chen, Luis de Sisternes & Daniel L. Rubin

  2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Qiang Chen

  3. Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, 94303, USA

    Theodore Leng

Authors
  1. Qiang Chen
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  2. Luis de Sisternes
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  3. Theodore Leng
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  4. Daniel L. Rubin
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Correspondence to Qiang Chen or Daniel L. Rubin.

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Chen, Q., de Sisternes, L., Leng, T. et al. Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images. J Digit Imaging 28, 346–361 (2015). https://doi.org/10.1007/s10278-014-9742-8

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  • DOI: https://doi.org/10.1007/s10278-014-9742-8

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