Enhancement of morphological and vascular features in OCT images using a modified Bayesian residual transform

Abstract

A novel image processing algorithm based on a modified Bayesian residual transform (MBRT) was developed for the enhancement of morphological and vascular features in optical coherence tomography (OCT) and OCT angiography (OCTA) images. The MBRT algorithm decomposes the original OCT image into multiple residual images, where each image presents information at a unique scale. Scale selective residual adaptation is used subsequently to enhance morphological features of interest, such as blood vessels and tissue layers, and to suppress irrelevant image features such as noise and motion artefacts. The performance of the proposed MBRT algorithm was tested on a series of cross-sectional and enface OCT and OCTA images of retina and brain tissue that were acquired in-vivo. Results show that the MBRT reduces speckle noise and motion-related imaging artefacts locally, thus improving significantly the contrast and visibility of morphological features in the OCT and OCTA images. compensation in optical coherence tomography using non-stationary spline-based speckle noise modelling,"

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

“…Different global and adaptive thresholding algorithms have been explored for automated segmentation of vasculature in fundus images and, more recently, in OCTA images, including Otsu's method [15][16][17] and multi-scale vesselness filters (e.g., Frangi filter). [22][23][24][25] However, translation of these thresholding algorithms to grayscale AOSLO perfusion images for the purposes of automatically segmenting retinal vasculature has proven challenging, primarily due to the large variations in contrast, brightness, and background signal that can typically manifest in AOSLO perfusion images. Machine learning techniques, such as convolutional neural networks (CNNs), have been developed for fundus [26][27][28] and OCTA 29 images.…”

Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?

“…The strong performance of OCT in detecting weak signals can be improved even more by image processing. Noise in OCT images limits the detection capabilities for weakly scattering structures, and thus a variety of different approaches for reducing OCT image noise have been proposed [7–15]. The high acquisition speeds of modern OCT systems enable the improvement of the signal-to-noise ratio by averaging multiple signals, for instance by fusing OCT frames or even volumes quickly repeated at the same sample position.…”

Automated thresholding algorithms outperform manual thresholding in macular optical coherence tomography angiography image analysis

“…filter to enhance image contrasts. We have omitted such steps in our analysis on purpose, since these methods have several disadvantages including generation of image artifacts resembling vessel structures and different results for vessels that are not equally distributed in size [39,40]. Limitations include the relatively small number of subjects (therefore limited generalizability of the comparisons between different automated algorithms), having only one repeat measurement per eye and limited comparability of vessel density and skeleton density values because of different calculation approaches in the literature.…”