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Pre-Processing Steps for Segmentation of Retinal Blood Vessels
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 94 - Number 12 |
| Year of Publication: 2014 |
| Authors: V. P. Patil, P. R. Wankhede |
10.5120/16398-6019
|
V. P. Patil, P. R. Wankhede . Pre-Processing Steps for Segmentation of Retinal Blood Vessels. International Journal of Computer Applications. 94, 12 ( May 2014), 34-37. DOI=10.5120/16398-6019
Abstract
Segmentation of blood vessels in retinal images is an important part in retinal image analysis for diagnosis and treatment of eye diseases for large screening systems. In this paper, we addressed the problem of background and noise extraction from retinal images. Blood vessels usually have central light reflex and poor local contrast, hence the results yield by blood vessel segmentation algorithms are not satisfactory. We used different preprocessing steps which includes central light reflex removal, background homogenization and vessel enhancement to make retinal image noise-free for post-processing. We used mean and Gaussian filtering along with Top-Hat transformation for noise extraction. The preprocessing steps were applied on 40 retinal images of DRIVE database available publically. Results show the darker retinal structures like blood vessels, fovea, and possible presence of microaneurysms or hemorrhages, get enhanced as compared to original retinal image and the brighter structures like optic disc and possible presence of exudates were get removed . The presented technique will definitely improve automatic fundus images analysis also be very useful to eye specialists in their visual examination of retina.
References
- Muhammad Moazam Fraz et al, "An Ensemble classification-based approach applied to blood vessel segmentation", IEEE Transactions on Biomedical Engineering, Vol. 59, No. 9, September 2012, pp 2538-2548.
- Arturo Aquino et al, " A new supervised Method for blood vessel segmentation in retinal images by using gray level and moment invariants-based features", IEEE Transactions on Medical Imaging, Vol. 30, No. 1 January 2011, pp 146-158.
- Niall Patton et al, " Retinal Image Analysis: Concepts, Applications and Potential", Progress in retinal in Retinal and Eye Research 25 (2006) 99-127.
- Kenneth W Tobin et al, "Detection of Anatomic Structures in Human Retinal Imagery", IEEE Transactions on Medical Imaging, Vol. 26, No. 12 December 2007 , pp 1729-1739.
- Delia Cabrera DeBuc, "A Review of Algorithms for Segmentation of Retinal Image Data Using Optical Coherence Tomography", Chapter 2, Image Segmentation, pp15-54, available at www. intechopen. com
- Marco Foracchia, "Luminosity and contrast normalization in retinal images", Medical Image Analysis 9 (2005) 179-190.
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