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Sparse And Low Rank Decomposition Based Batch Image Alignment for Speckle Reduction of retinal OCT Images

14 November 2014
Ahmadreza Baghaie
R. D'Souza
Zeyun Yu
ArXiv (abs)PDFHTML
Abstract

Optical Coherence Tomography (OCT) in an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Here, a sparse and low rank decomposition based method is used for speckle reduction in retinal OCT images. In this technique, at first, several OCT images of the same location (to some extent) are acquired and stacked together. The next step is the batch alignment of the images using a sparse and low-rank decomposition based technique, usually used in surveillance video processing for the separation of foreground and background. Finally the denoise image is created by median filtering of the low-rank component of the processed data. Simultaneous decomposition and alignment of the images result in better performance in comparison to simple registration-based methods that are used in the literature for noise reduction of OCT images.

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