Super-resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling.

Background: Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT-assisted diagnostics. In this article, we address the super-resolution (SR) problem of retinal OCT images using a statistical modeling point of view.

Statistical modeling of retinal optical coherence tomography using the Weibull mixture model.

In this paper, a novel statistical model is proposed for retinal optical coherence tomography (OCT) images. According to the layered structure of the retina, a mixture of six Weibull distributions is proposed to describe the main statistical features of OCT images. We apply Weibull distribution to establish a more comprehensive model but with fewer parameters that has better goodness of fit (GoF) than previous models.

OCT Image Denoising Based on Asymmetric Normal Laplace Mixture Model.

Optical Coherence Tomography (OCT) is one of the well-known imaging systems in ophthalmology that provides images with high resolution from retinal tissue. However, like other coherent imaging systems, OCT images suffer from speckle noise which decreases the image quality. Denoising can be considered as an estimation problem in a Bayesian framework.

Statistical modeling of Optical Coherence Tomography images by asymmetric Normal Laplace mixture model.

Optical Coherence Tomography (OCT) is known as a non-invasive and high resolution imaging modality in ophthalmology. Effecting noise on the OCT images as well as other reasons cause a random behavior in these images. In this study, we introduce a new statistical model for retinal layers in healthy OCT images.

Isfahan MISP Dataset.

An online depository was introduced to share clinical ground truth with the public and provide open access for researchers to evaluate their computer-aided algorithms. PHP was used for web programming and MySQL for database managing. The website was entitled "biosigdata.

Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation.

Optical Coherence Tomography (OCT) is one of the most informative methodologies in ophthalmology and provides cross sectional images from anterior and posterior segments of the eye. Corneal diseases can be diagnosed by these images and corneal thickness maps can also assist in the treatment and diagnosis. The need for automatic segmentation of cross sectional images is inevitable since manual segmentation is time consuming and imprecise.