Abstract
An important image post-processing step for optical coherence tomography (OCT) images is speckle noise reduction. Noise in OCT images is multiplicative in nature and is difficult to suppress due to the fact that in addition the noise component, OCT speckle also carries structural information about the imaged object. To address this issue, a novel speckle noise reduction algorithm was developed. The algorithm projects the imaging data into the logarithmic space and a general Bayesian least squares estimate of the noise-free data is found using a conditional posterior sampling approach. The proposed algorithm was tested on a number of rodent (rat) retina images acquired in-vivo with an ultrahigh resolution OCT system. The performance of the algorithm was compared to that of the state-of-the-art algorithms currently available for speckle denoising, such as the adaptive median, maximum a posteriori (MAP) estimation, linear least squares estimation, anisotropic diffusion and wavelet-domain filtering methods. Experimental results show that the proposed approach is capable of achieving state-of-the-art performance when compared to the other tested methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge preservation, and equivalent number of looks (ENL) measures. Visual comparisons also show that the proposed approach provides effective speckle noise suppression while preserving the sharpness and improving the visibility of morphological details, such as tiny capillaries and thin layers in the rat retina OCT images.
Highlights
Speckle is an inherent characteristic of images acquired with any imaging technique that is based on detection of coherent waves, for example synthetic aperture radar (SAR), ultrasound, coherent optical imaging, etc
Such small structural details can be obscured by the presence of speckle noise in unprocessed ultrahigh resolution OCT (UHROCT) images, or by the blurring and / or image artefacts introduced by speckle denoising algorithms that are currently used in commercial and research grade Optical coherence tomography (OCT) systems or have been published in the past
The images were acquired with a state-of-the art, research grade high speed, UHROCT system operating in the 1060nm wavelength region
Summary
Speckle is an inherent characteristic of images acquired with any imaging technique that is based on detection of coherent waves, for example synthetic aperture radar (SAR), ultrasound, coherent optical imaging, etc. With the development of ultrahigh resolution OCT (UHROCT), cellular level resolution can be achieved in biological tissue This enables the visualization of small morphological details in the UHROCT tomograms such as individual tissue layers, small blood and lymph vessels, calcifications, lipid deposits, small clusters of highly specialized cells, etc. The development of fast speckle noise reduction algorithms for UHROCT with very good preservation of boundaries of layered structure or small morphological features is of high importance Such algorithms can improve the quality of the visual appearance of UHROCT images and allow for zooming on small features in the image without compromising the sharpness of the details or the overall image quality. Speckle noise reduction in OCT images is challenging, because of the difficulty in separating the noise and the information components in the speckle pattern
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