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J Digit Imaging. 2015 Jun;28(3):346-61. doi: 10.1007/s10278-014-9742-8.

Application of improved homogeneity similarity-based denoising in optical coherence tomography retinal images.

Author information

1
Department of Radiology, Stanford University, Stanford, CA, 94305, USA, chen2qiang@njust.edu.cn.

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.

PMID:
25404105
PMCID:
PMC4441691
DOI:
10.1007/s10278-014-9742-8
[Indexed for MEDLINE]
Free PMC Article

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