Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Analysis of Fundus Fluorescein Angiogram Based on the Hessian Matrix of Directional Curvelet Sub-Bands and Distance Regularized Level Set Evolution Publisher



Soltanipour A1 ; Sadri S1, 2 ; Rabbani H2 ; Akhlaghi MR3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
  2. 2. Department of Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Ophthalmology, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Medical Signals and Sensors Published:2015


Abstract

This paper presents a new procedure for automatic extraction of the blood vessels and optic disk (OD) in fundus fluorescein angiogram (FFA). In order to extract blood vessel centerlines, the algorithm of vessel extraction starts with the analysis of directional images resulting from sub-bands of fast discrete curvelet transform (FDCT) in the similar directions and different scales. For this purpose, each directional image is processed by using information of the first order derivative and eigenvalues obtained from the Hessian matrix. The final vessel segmentation is obtained using a simple region growing algorithm iteratively, which merges centerline images with the contents of images resulting from modified top-hat transform followed by bit plane slicing. After extracting blood vessels from FFA image, candidate regions for OD are enhanced by removing blood vessels from the FFA image, using multi-structure elements morphology, and modification of FDCT coefficients. Then, canny edge detector and Hough transform are applied to the reconstructed image to extract the boundary of candidate regions. At the next step, the information of the main arc of the retinal vessels surrounding the OD region is used to extract the actual location of the OD. Finally, the OD boundary is detected by applying distance regularized level set evolution. The proposed method was tested on the FFA images from angiography unit of Isfahan Feiz Hospital, containing 70 FFA images from different diabetic retinopathy stages. The experimental results show the accuracy more than 93% for vessel segmentation and more than 87% for OD boundary extraction. © 2015, Isfahan University of Medical Sciences(IUMS). All rights reserved.
1. Vessel Centerlines Extraction From Fundus Fluorescein Angiogram Based on Hessian Analysis of Directional Curvelet Subbands, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (2013)
3. Extraction of Retinal Blood Vessels by Curvelet Transform, Proceedings - International Conference on Image Processing, ICIP (2009)
6. Retinal Vessel Segmentation Using System Fuzzy and Dbscan Algorithm, 2015 2nd International Conference on Pattern Recognition and Image Analysis, IPRIA 2015 (2015)
7. Circlet Based Framework for Optic Disk Detection, Proceedings - International Conference on Image Processing, ICIP (2017)
Experts (# of related papers)
Other Related Docs
9. Diabetic Retinopathy Grading by Digital Curvelet Transform, Computational and Mathematical Methods in Medicine (2012)
10. 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)
12. Alignment of Optic Nerve Head Optical Coherence Tomography B-Scans in Right and Left Eyes, Proceedings - International Conference on Image Processing, ICIP (2017)
18. Vessel Segmentation in Images of Optical Coherence Tomography Using Shadow Information and Thickening of Retinal Nerve Fiber Layer, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (2013)
20. A New Curvelet Transform Based Method for Extraction of Red Lesions in Digital Color Retinal Images, Proceedings - International Conference on Image Processing, ICIP (2010)
21. Analysis of Foveal Avascular Zone for Grading of Diabetic Retinopathy Severity Based on Curvelet Transform, Graefe's Archive for Clinical and Experimental Ophthalmology (2012)
23. Automatic Detection of Micro-Aneurysms in Retinal Images Based on Curvelet Transform and Morphological Operations, Proceedings of SPIE - The International Society for Optical Engineering (2013)
26. A Computationally Efficient Red-Lesion Extraction Method for Retinal Fundus Images, IEEE Transactions on Instrumentation and Measurement (2023)
30. 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)
33. Asymmetry Evaluation of Fundus Images in Right and Left Eyes Using Radon Transform and Fractal Analysis, Proceedings - International Conference on Image Processing, ICIP (2015)
34. Circlet Based Framework for Red Blood Cells Segmentation and Counting, IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation (2015)
37. Vesselness-Guided Active Contour: A Coronary Vessel Extraction Method, Journal of Medical Signals and Sensors (2014)
43. Automatic Detection of Microaneurysms in Oct Images Using Bag of Features, Computational and Mathematical Methods in Medicine (2022)
45. Detection and Registration of Vessels of Fundus and Oct Images Using Curevelet Analysis, IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012 (2012)
46. Automatic Detection of Hyperreflective Foci in Optical Coherence Tomography B-Scans Using Morphological Component Analysis, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2017)
48. Non-Rigid Registration of Fluorescein Angiography and Optical Coherence Tomography Via Scanning Laser Ophthalmoscope Imaging, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2017)