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Statistical Modeling of Optical Coherence Tomography Images by Asymmetric Normal Laplace Mixture Model Publisher Pubmed



Jorjandi S1 ; Rabbani H2 ; Kafieh R2 ; Amini Z2
Authors

Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS Published:2017


Abstract

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. This model, namely asymmetric Normal Laplace (NL), fits well the advent of asymmetry and heavy-tailed in intensity distribution of each layer. Due to the layered structure of retina, a mixture model is addressed. It is proposed to evaluate the fitness criteria called Kull-back Leibler Divergence (KLD) and chi-square test along visual results. The results express the well performance of proposed model in fitness of data except for 6th and 7th layers. Using a complicated model, e.g. a mixture model with two component, seems to be appropriate for these layers. The mentioned process for train images can then be devised for a test image by employing the Expectation Maximization (EM) algorithm to estimate the values of parameters in mixture model. © 2017 IEEE.
1. Oct Image Denoising Based on Asymmetric Normal Laplace Mixture Model, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS (2019)
2. Statistical Modeling of Retinal Optical Coherence Tomography, IEEE Transactions on Medical Imaging (2016)
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