Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Stochastic Differential Equations for Automatic Quality Control of Retinal Optical Coherence Tomography Images Publisher Pubmed



Tajmirriahi M1 ; Rostamian R1 ; Amini Z1 ; Hamidi A2 ; Zam A3 ; Rabbani H1
Authors
Show Affiliations
Authors Affiliations
  1. 1. Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. University of Basel, Biomedical Laser and Optics Group (BLOG), Department of Biomedical Engineering, Switzerland
  3. 3. New York University, United States

Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Published:2022


Abstract

Optical coherence tomography is widely used to provide high resolution images from retina. During data acquisition, several artifacts may be associated with OCT images which clearly remove information of retinal layers and degrade the quality of images. Manual assessment of the acquired OCT images is hard and time consuming. Therefore, an automatic quality control step is necessary to detect poor images for next decisions of eliminating them and even re-scanning. In this study, a novel automatic quality control methodology is proposed for early assessment of the OCT images quality by employing stochastic differential equations (SDE). In this method -stable nature of OCT images is represented by applying a fractional Laplacian filter and parameters of the obtained -stable are fed to an SVM to automatically detect high quality vs poor quality images. The simulation results on a large dataset of normal and abnormal OCT images show that proposed method has outstanding performance in detection of poor vs high quality images. The methodology is applicable to the image quality assessment of other OCT scanning devices as well. Clinical Relevance - Automatic quality control assessment of retinal OCT images provides reliable data for diagnosis of retinal and systematic diseases in clinical applications. © 2022 IEEE.
Related Docs
View other Related Docs
1. Mixture of Symmetric Stable Distributions for Macular Pathology Detection in Optical Coherence Tomography Scans, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2022)
2. Modeling of Retinal Optical Coherence Tomography Based on Stochastic Differential Equations: Application to Denoising, IEEE Transactions on Medical Imaging (2021)
3. Curvature Correction of Retinal Octs Using Graph-Based Geometry Detection, Physics in Medicine and Biology (2013)
4. Local Self-Similar Solution of Admm for Denoising of Retinal Oct Images, IEEE Transactions on Instrumentation and Measurement (2024)
5. Discrimination of Multiple Sclerosis Using Oct Images From Two Different Centers, Multiple Sclerosis and Related Disorders (2023)
6. Cohort Profile: A Collaborative Multicentre Study of Retinal Optical Coherence Tomography in 539 Patients With Neuromyelitis Optica Spectrum Disorders (Croctino), BMJ Open (2020)
7. Introduction to Optical Coherence Tomography, Atlas of Ocular Optical Coherence Tomograph, Second Edition (2023)
8. Super-Resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling, Journal of Medical Signals and Sensors (2024)
Experts (# of related papers)
View all Related Experts
Hossein Rabbani (79)
Raheleh Kafieh (41)
Other Related Docs
9. Optical Oherence Tomography Image Reconstruction Using Morphological Component Analysis, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2019)
10. Automatic Production of Synthetic Labelled Oct Images Using an Active Shape Model Hajar Danesh, IET Image Processing (2020)
11. 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)
12. Analysis of a Novel Segmentation Algorithm for Optical Coherence Tomography Images Based on Pixels Intensity Correlations, IEEE Transactions on Instrumentation and Measurement (2021)
13. Synthetic Oct Data in Challenging Conditions: Three-Dimensional Oct and Presence of Abnormalities, Medical and Biological Engineering and Computing (2022)
14. Statistical Modeling of Retinal Optical Coherence Tomography, IEEE Transactions on Medical Imaging (2016)
15. X-Let's Atom Combinations for Modeling and Denoising of Oct Images by Modified Morphological Component Analysis, IEEE Transactions on Medical Imaging (2024)
16. Multivariate Statistical Modeling of Retinal Optical Coherence Tomography, IEEE Transactions on Medical Imaging (2020)
17. A Review on Texture-Based Methods for Anomaly Detection in Retinal Optical Coherence Tomography Images, Optik (2023)
18. 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)
19. 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)
20. A New Texture-Based Labeling Framework for Hyper-Reflective Foci Identification in Retinal Optical Coherence Tomography Images, Scientific Reports (2024)
21. 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)
22. Three-Dimensional Segmentation of Retinal Cysts From Spectral-Domain Optical Coherence Tomography Images by the Use of Three-Dimensional Curvelet Based K-Svd, Journal of Medical Signals and Sensors (2016)
23. Vessel-Based Registration of Fundus and Optical Coherence Tomography Projection Images of Retina Using a Quadratic Registration Model, IET Image Processing (2013)
24. Automatic Choroidal Segmentation in Optical Coherence Tomography Images Based on Curvelet Transform and Graph Theory, Journal of Medical Signals and Sensors (2023)
25. 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)
26. Exact Localization of Breakpoints of Retinal Pigment Epithelium in Optical Coherence Tomography of Optic Nerve Head, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2017)
27. A New Convolutional Neural Network Based on Combination of Circlets and Wavelets for Macular Oct Classification, Scientific Reports (2023)
28. Astrocytic Outer Retinal Layer Thinning Is Not a Feature in Aqp4-Igg Seropositive Neuromyelitis Optica Spectrum Disorders, Journal of Neurology, Neurosurgery and Psychiatry (2022)
29. Hybrid Registration of Retinal Fluorescein Angiography and Optical Coherence Tomography Images of Patients With Diabetic Retinopathy, Biomedical Optics Express (2021)
30. Automatic Multifaceted Matlab Package for Analysis of Ocular Images (Ampao), SoftwareX (2019)
31. Registration of Fluorescein Angiography and Optical Coherence Tomography Images of Curved Retina Via Scanning Laser Ophthalmoscopy Photographs, Biomedical Optics Express (2020)
32. Three-Dimensional Optical Coherence Tomography Image Denoising Through Multi-Input Fully-Convolutional Networks, Computers in Biology and Medicine (2019)
33. The Improvement of Extraction Retinal Pigment Epithelium (Rpe) Layer in Optical Coherence Tomography (Oct) Images by Using Piecewise Dynamic Programming Method in Patients With Persistent Epithelial Defect (Ped), Journal of Isfahan Medical School (2021)
34. Statistical Modeling of Optical Coherence Tomography Images by Asymmetric Normal Laplace Mixture Model, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2017)
35. Classification of Dry Age-Related Macular Degeneration and Diabetic Macular Oedema From Optical Coherence Tomography Images Using Dictionary Learning, IET Image Processing (2020)
36. Total Variation Regularized Tensor Ring Decomposition for Oct Image Denoising and Super-Resolution, Computers in Biology and Medicine (2024)
37. Linear Multifractional Stable Motion for Modeling of Fluid-Filled Regions in Retinal Optical Coherence Tomography Images, Chaos, Solitons and Fractals (2024)
38. Wavelet Scattering Transform Application in Classification of Retinal Abnormalities Using Oct Images, Scientific Reports (2023)
39. Cyst Identification in Retinal Optical Coherence Tomography Images Using Hidden Markov Model, Scientific Reports (2023)
40. Retinal Changes in Double-Antibody Seronegative Neuromyelitis Optica Spectrum Disorders, Neurology: Neuroimmunology and NeuroInflammation (2024)
41. Directional Analysis of Intensity Changes for Determining the Existence of Cyst in Optical Coherence Tomography Images, Scientific Reports (2022)
42. Formation and Fusion of Projection Images From 11 Layers of Retina Using Statistical Indicators to Obtain an Image With Appropriate Contrast From the Retinal Depth, Journal of Isfahan Medical School (2013)
43. Automatic Detection of Exudates and Optic Disk in Retinal Images Using Curvelet Transform, IET Image Processing (2012)
44. A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images, Journal of Medical Signals and Sensors (2017)
45. Forming Optimal Projection Images From Intra-Retinal Layers Using Curvelet-Based Image Fusion Method, Journal of Medical Signals and Sensors (2020)
46. Modeling of Seizure and Seizure-Free Eeg Signals Based on Stochastic Differential Equations, Chaos, Solitons and Fractals (2021)
47. Retinal Optical Coherence Tomography Image Analysis by a Restricted Boltzmann Machine, Biomedical Optics Express (2022)
48. 3D Curvelet-Based Segmentation & Quantification of Drusen in Optical Coherence Tomography Images, Journal of Electrical and Computer Engineering (2017)
49. Morphological Component Analysis for Automatic Segmentation of Six Lower Retina Layers in Optical Coherence Tomography Images, Journal of Isfahan Medical School (2016)
50. Reconstruction of Optical Coherence Tomography Images Using Mixed Low Rank Approximation and Second Order Tensor Based Total Variation Method, IEEE Transactions on Medical Imaging (2021)