Oct Image Denoising Based on Asymmetric Normal Laplace Mixture Model Publisher Pubmed

Summary: A study developed a method to reduce noise in eye scans, improving clarity for better diagnosis. #EyeHealth #MedicalImaging

Jorjandi S¹ ; Rabbani H² ; Amini Z² ; Kafieh R²
Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Published:2019

Abstract

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. So, finding a suitable distribution for noiseless data is an important issue. We propose a statistical model for OCT data, namely Asymmetric Normal Laplace Mixture Model (ANLMM), and then convert its distribution to normal by Gaussianization Transform (GT). Finally, by applying the Spatially Constrained Gaussian Mixture Model (SC-GMM), a new OCT denoising algorithm is introduced, which significantly outperforms the other methods in terms of Contrast-to-Noise Ratio (CNR). © 2019 IEEE.