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Title: Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering
Authors: Palanisamy, P.
Rajan, Jeny
Xiaojun, Yu
Sudeep, P. V.
Issac Niwas, Swamidoss
Wang, Xianghong
Luo, Yuemei
Liu, Linbo
Keywords: Bilateral filtering
Issue Date: 2016
Source: Sudeep, P. V., Issac Niwas, S., Palanisamy, P., Rajan, J., Xiaojun, Y., Wang, X., et al. (2016). Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering. Computers in Biology and Medicine, 71, 97-107.
Series/Report no.: Computers in Biology and Medicine
Abstract: Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter.
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2016.02.003
Rights: © 2016 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Computers in Biology and Medicine, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
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