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Computational and Mathematical Methods in Medicine
Volume 2017 (2017), Article ID 1769834, 13 pages
https://doi.org/10.1155/2017/1769834
Research Article

Retinal Image Denoising via Bilateral Filter with a Spatial Kernel of Optimally Oriented Line Spread Function

1School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
2Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
3Institute of Life Sciences, Shandong Normal University, Jinan 250014, China
4Key Laboratory of Intelligent Information Processing, Shandong Normal University, Jinan 250014, China
5School of Psychology, Shandong Normal University, Jinan 250014, China

Correspondence should be addressed to Yuanjie Zheng

Received 25 August 2016; Revised 30 November 2016; Accepted 13 December 2016; Published 5 February 2017

Academic Editor: Marc Thilo Figge

Copyright © 2017 Yunlong He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Filtering belongs to the most fundamental operations of retinal image processing and for which the value of the filtered image at a given location is a function of the values in a local window centered at this location. However, preserving thin retinal vessels during the filtering process is challenging due to vessels’ small area and weak contrast compared to background, caused by the limited resolution of imaging and less blood flow in the vessel. In this paper, we present a novel retinal image denoising approach which is able to preserve the details of retinal vessels while effectively eliminating image noise. Specifically, our approach is carried out by determining an optimal spatial kernel for the bilateral filter, which is represented by a line spread function with an orientation and scale adjusted adaptively to the local vessel structure. Moreover, this approach can also be served as a preprocessing tool for improving the accuracy of the vessel detection technique. Experimental results show the superiority of our approach over state-of-the-art image denoising techniques such as the bilateral filter.