Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
A New Combined Method Based on Curvelet Transform and Morphological Operators for Automatic Detection of Foveal Avascular Zone Publisher



Hajeb Mohammad Alipour S1 ; Rabbani H1 ; Akhlaghi M2
Authors
Show Affiliations
Authors Affiliations
  1. 1. Biomedical Engineering Department, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Ophthalmology Department School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Signal, Image and Video Processing Published:2014


Abstract

In order to achieve early detection of diabetic retinopathy (DR) for the sake of preventing from blindness, regular screening using retinal photography is necessary. Abnormalities of DR do not have uniform distribution over the retina. Certain types of abnormalities usually occur in specific areas on the retina. The distance between lesions, such as micro-aneurysms, and the foveal avascular zone (FAZ) is a useful feature for later analysis and grading of DR. In this paper, a new fully automatic system is presented to find the location of FAZ in fundus fluorescein angiogram photographs. The method is based on two procedures: digital curvelet transform (DCUT) and morphological operations. Firstly, end points of vessels are detected based on vessel segmentation using DCUT. By connecting these points in the selected region of interest, FAZ region is extracted. Secondly, vessels are subtracted from the retinal image, and morphological dilatation and erosion are applied on the resulted image. By choosing an appropriate threshold, FAZ region is detected. The final FAZ region is extracted by performing logical AND between two segmented FAZ. Our experiments show that the system achieves, respectively, the specificity and sensitivity of (>98 and >96 %) for normal stage, for mild/moderate non-proliferative DR (NPDR) (>98, and >95 %) and for Sever NPDR + PDR (>97 and >93 %). © 2013 Springer-Verlag London.
1. Analysis of Foveal Avascular Zone for Grading of Diabetic Retinopathy Severity Based on Curvelet Transform, Graefe's Archive for Clinical and Experimental Ophthalmology (2012)
2. Diabetic Retinopathy Grading by Digital Curvelet Transform, Computational and Mathematical Methods in Medicine (2012)
6. A New Curvelet Transform Based Method for Extraction of Red Lesions in Digital Color Retinal Images, Proceedings - International Conference on Image Processing, ICIP (2010)
7. 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)
8. Extraction of Retinal Blood Vessels by Curvelet Transform, Proceedings - International Conference on Image Processing, ICIP (2009)
Experts (# of related papers)
Other Related Docs
9. Automatic Detection of the Optic Disc of the Retina: A Fast Method, Journal of Medical Signals and Sensors (2016)
13. Automatic Detection of Microaneurysms in Oct Images Using Bag of Features, Computational and Mathematical Methods in Medicine (2022)
16. 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)
19. A Computationally Efficient Red-Lesion Extraction Method for Retinal Fundus Images, IEEE Transactions on Instrumentation and Measurement (2023)
20. Circlet Based Framework for Optic Disk Detection, Proceedings - International Conference on Image Processing, ICIP (2017)
21. 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)
23. A Dictionary Learning Based Method for Detection of Diabetic Retinopathy in Color Fundus Images, Iranian Conference on Machine Vision and Image Processing, MVIP (2017)
27. Detection and Registration of Vessels of Fundus and Oct Images Using Curevelet Analysis, IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012 (2012)
36. Retinal Vessel Segmentation Using System Fuzzy and Dbscan Algorithm, 2015 2nd International Conference on Pattern Recognition and Image Analysis, IPRIA 2015 (2015)
38. Asymmetry Evaluation of Fundus Images in Right and Left Eyes Using Radon Transform and Fractal Analysis, Proceedings - International Conference on Image Processing, ICIP (2015)
39. Classification of Three Types of Red Blood Cells in Peripheral Blood Smear Based on Morphology, International Conference on Signal Processing Proceedings, ICSP (2010)
42. Detection of Retinal Abnormalities in Oct Images Using Wavelet Scattering Network, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2022)
49. 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)
50. 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)