Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share By
Mathematical Analysis of Texture Indicators for the Segmentation of Optical Coherence Tomography Images Publisher

Summary: Scientists report a new model improves retinal layer detection in OCT images, aiding eye disease diagnosis. #EyeHealth #MedicalImaging

Monemian M1 ; Rabbani H1
Authors

Source: Optik Published:2020


Abstract

Optical Coherence Tomography (OCT) is a non-invasive technology which facilitates the process of capturing images from light-scattering organs like retina. Retina is a layered structure each layer of which has its own morphological properties. The segmentation of retinal layers helps to identify retinal diseases. In this paper, a novel mathematical model is proposed which can extract boundary pixels located on the borders between layers. The new model uses texture properties of pixels to extract distinguishing characteristics for boundary pixels. It is explored that boundary pixels provide certain values for texture indicators leading to the existence of special relation between neighbor pixels’ intensities. Using the new model which is based on Laplace distribution, it is possible to compute the probability of being a boundary pixel for each pixel. The numerical results show that the proposed model is capable of identifying retinal layers’ boundaries in normal cases with acceptable accuracy. © 2020 Elsevier GmbH
Related Docs
View other Related Docs
1. Analysis of a Novel Segmentation Algorithm for Optical Coherence Tomography Images Based on Pixels Intensity Correlations, IEEE Transactions on Instrumentation and Measurement (2021)
2. Texture Modeling in Optical Coherence Tomography Images, Handbook of Texture Analysis: Generalized Texture for AI-Based Industrial Applications (2024)
3. A New Texture-Based Segmentation Method for Optical Coherence Tomography Images, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2019)
Experts (# of related papers)
View all Related Experts
Hossein Rabbani (20)
Raheleh Kafieh (7)
Mathematical Analysis of Texture Indicators for the Segmentation of Optical Coherence Tomography Images
Other Related Docs
4. Automatic Diagnosis of Abnormal Macula in Retinal Optical Coherence Tomography Images Using Wavelet-Based Convolutional Neural Network Features and Random Forests Classifier, Journal of Biomedical Optics (2018)
5. Statistical Modeling of Retinal Optical Coherence Tomography Using the Weibull Mixture Model, Biomedical Optics Express (2021)
6. Modeling of Retinal Optical Coherence Tomography Based on Stochastic Differential Equations: Application to Denoising, IEEE Transactions on Medical Imaging (2021)
7. 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)
8. Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms, IEEE Transactions on Biomedical Engineering (2018)
9. A Review of Algorithms for Segmentation of Optical Coherence Tomography From Retina, Journal of Medical Signals and Sensors (2013)
10. An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images Using Gaussian Mixture Model, Journal of Medical Signals and Sensors (2014)
11. Super-Resolution of Optical Coherence Tomography Images by Scale Mixture Models, IEEE Transactions on Image Processing (2020)
12. Optimal Deep Learning Architecture for Automated Segmentation of Cysts in Oct Images Using X-Let Transforms, Diagnostics (2023)
13. Intra-Retinal Layer Segmentation of 3D Optical Coherence Tomography Using Coarse Grained Diffusion Map, Medical Image Analysis (2013)
14. Livelayer: A Semi-Automatic Software Program for Segmentation of Layers and Diabetic Macular Edema in Optical Coherence Tomography Images, Scientific Reports (2021)
15. Morphological Component Analysis for Automatic Segmentation of Six Lower Retina Layers in Optical Coherence Tomography Images, Journal of Isfahan Medical School (2016)
16. Curvature Correction of Retinal Octs Using Graph-Based Geometry Detection, Physics in Medicine and Biology (2013)
17. Retinal Optical Coherence Tomography Image Analysis by a Restricted Boltzmann Machine, Biomedical Optics Express (2022)
18. Directional Analysis of Intensity Changes for Determining the Existence of Cyst in Optical Coherence Tomography Images, Scientific Reports (2022)
19. Multivariate Statistical Modeling of Retinal Optical Coherence Tomography, IEEE Transactions on Medical Imaging (2020)
20. Forming Projection Images From Each Layer of Retina Using Diffusion May Based Oct Segmentation, 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 (2012)