Isfahan University of Medical Sciences

Science Communicator Platform

Share By
Forming Projection Images From Each Layer of Retina Using Diffusion May Based Oct Segmentation Publisher



Jalili J1 ; Rabbani H1 ; Akhlaghi M2 ; Kafieh R1 ; Mehridehnavi A1
Authors

Source: 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 Published:2012


Abstract

Optical coherence tomography (OCT) is an effective and noninvasive modality for retinal imaging. 3-D data that acquired from 3-D Spectral Domain OCT (SD-OCT) have shown their importance in the evaluation of retinal diseases. In addition, this set of data provides an opportunity to study depth of retina. In this paper, we focus on forming X-Y axis images from each layer of retina. In this manner, we first choose diffusion map based segmentation for localization of 12 different boundaries in 3D retinal data. Then we take an average on layers which located between each pairs of detected boundaries. Therefore, we make the X-Y axis image from each layer. With wavelet based image fusion, we combine together the layers with appropriate information to make images with additional information in retinal depth. © 2012 IEEE.
Related Docs
View other Related Docs
1. 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)
2. Mathematical Analysis of Texture Indicators for the Segmentation of Optical Coherence Tomography Images, Optik (2020)
3. Forming Optimal Projection Images From Intra-Retinal Layers Using Curvelet-Based Image Fusion Method, Journal of Medical Signals and Sensors (2020)
Experts (# of related papers)
View all Related Experts
Hossein Rabbani (14)
Raheleh Kafieh (6)
Other Related Docs
4. Optimal Deep Learning Architecture for Automated Segmentation of Cysts in Oct Images Using X-Let Transforms, Diagnostics (2023)
5. Automatic Classification of Macular Diseases From Oct Images Using Cnn Guided With Edge Convolutional Layer, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2022)
6. A Review on Texture-Based Methods for Anomaly Detection in Retinal Optical Coherence Tomography Images, Optik (2023)
7. Intra-Retinal Layer Segmentation of Optical Coherence Tomography Using Diffusion Map, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (2013)
8. Automatic Optic Disk Detection by the Use of Curvelet Transform, Final Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009 (2009)
9. Thickness Mapping of Eleven Retinal Layers Segmented Using the Diffusion Maps Method in Normal Eyes, Journal of Ophthalmology (2015)
10. Detection and Registration of Vessels of Fundus and Oct Images Using Curevelet Analysis, IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012 (2012)
11. Automatic Optic Disk Boundary Extraction by the Use of Curvelet Transform and Deformable Variational Level Set Model, Pattern Recognition (2012)
12. A Device-Independent, Shape Preserving Retinal Optical Coherence Tomography Image Alignment Method Applying Tv-Unet for Rpe Layer Detection, IEEE Transactions on Instrumentation and Measurement (2022)
13. Local Comparison of Cup to Disc Ratio in Right and Left Eyes Based on Fusion of Color Fundus Images and Oct B-Scans, Information Fusion (2019)
14. Statistical Modeling of Retinal Optical Coherence Tomography, IEEE Transactions on Medical Imaging (2016)