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Proceedings Paper

Geodesic denoising for optical coherence tomography images
Author(s): Ehsan Shahrian Varnousfaderani; Wolf-Dieter Vogl; Jing Wu; Bianca S. Gerendas; Christian Simader; Georg Langs; Sebastian M. Waldstein; Ursula Schmidt-Erfurth
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Paper Abstract

Optical coherence tomography (OCT) is an optical signal acquisition method capturing micrometer resolution, cross-sectional three-dimensional images. OCT images are used widely in ophthalmology to diagnose and monitor retinal diseases such as age-related macular degeneration (AMD) and Glaucoma. While OCT allows the visualization of retinal structures such as vessels and retinal layers, image quality and contrast is reduced by speckle noise, obfuscating small, low intensity structures and structural boundaries. Existing denoising methods for OCT images may remove clinically significant image features such as texture and boundaries of anomalies. In this paper, we propose a novel patch based denoising method, Geodesic Denoising. The method reduces noise in OCT images while preserving clinically significant, although small, pathological structures, such as fluid-filled cysts in diseased retinas. Our method selects optimal image patch distribution representations based on geodesic patch similarity to noisy samples. Patch distributions are then randomly sampled to build a set of best matching candidates for every noisy sample, and the denoised value is computed based on a geodesic weighted average of the best candidate samples. Our method is evaluated qualitatively on real pathological OCT scans and quantitatively on a proposed set of ground truth, noise free synthetic OCT scans with artificially added noise and pathologies. Experimental results show that performance of our method is comparable with state of the art denoising methods while outperforming them in preserving the critical clinically relevant structures.

Paper Details

Date Published: 21 March 2016
PDF: 11 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840K (21 March 2016); doi: 10.1117/12.2216972
Show Author Affiliations
Ehsan Shahrian Varnousfaderani, Univ. of California, San Diego (United States)
Wolf-Dieter Vogl, Medizinische Univ. Wien (Austria)
Jing Wu, Medizinische Univ. Wien (Austria)
Bianca S. Gerendas, Medizinische Univ. Wien (Austria)
Christian Simader, Medizinische Univ. Wien (Austria)
Georg Langs, Vienna Reading Ctr. (Austria)
Sebastian M. Waldstein, Medizinische Univ. Wien (Austria)
Ursula Schmidt-Erfurth, Vienna Reading Ctr. (Austria)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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