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

Summary: A study proposes a new model for clearer eye scans, aiding early disease detection. #EyeHealth #MedicalImaging

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)
3. Retinal Oct Image Denoising Based on Adaptive Bessel K-Form Modeling, 2023 30th National and 8th International Iranian Conference on Biomedical Engineering, ICBME 2023 (2023)
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Statistical Modeling of Optical Coherence Tomography Images by Asymmetric Normal Laplace Mixture Model
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