Analysis of Retinal Vasculature by Watershed Segmentation and Histogram Analysis

Analysis of Retinal Vasculature by Watershed Segmentation and Histogram Analysis

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-45 Number-5
Year of Publication : 2017
Authors : Soundarya.M.S, Swathi.T
DOI :  10.14445/22315381/IJETT-V45P249

Citation 

Soundarya.M.S, Swathi.T "Analysis of Retinal Vasculature by Watershed Segmentation and Histogram Analysis", International Journal of Engineering Trends and Technology (IJETT), V45(5),237-240 March 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Retinal image analysis is becoming eminent as a nonintrusive diagnosis method in modern ophthalmology. This paper is mainly focused on the early diagnosis of diabetic retinopathy by analysing and detecting of vascular structures in retinal images. When small vessels in the retina have high level of glucose, it produces blur vision which eventually leads to blindness. Usually retinal images are taken from DRIVE dataset. The small vessels which are abnormal are not visible by naked eye are segmented accurately by watershed segmentation. Through watershed segmentation we enhance the blood vessel and suppress the background information. The segmented abnormal nerve image is compared with the healthy and normal nerve image through histogram equalization. Experimental results achieved from the proposed method effectively used to reduce the time for the ophthalmologist to detect disease and give accurate treatment.

 References

[1] Hemant P.Chaudhary,Amol D.Rahulkar,“Segmentation of Retinal Vessels by the Use of Gabor Wavelet and Linear Mean Squared Error Classifier”, International Journal of Emerging Engineering Research and Technology, Volume 2, Issue 2,PP 119-125, May 2014.
[2] , A.Aquino, M. E. Gegundez-Arias, J. M. Bravo, ", “A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features "”, Medical Imaging, IEEE Transactions on , vol.30, no.1, pp.146-158, Jan. 2011.
[3] M. Al-Rawi, M. Qutaishat, M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images”, 2007.
[4] T. Zhu, “Fourier cross-sectional profile for vessel detection on retinal images”, Computerized medical imaging and graphics, Vol. 34, pp. 203– 212, 2010.
[5] J.B.T.M. Roerdink and A. Meijster, „”The watershed transform: definitions, algorithms, and parallelization strategies”,FundamentaInformaticae 41, pp. 187–228, 2000.
[6] Sameer Ruparelia, “Implementation of Watershed Based Image Segmentation Algorithm in FPGA”, 2012.
[7] Meyer, F, “Topographic distance and watershed lines”, Signal Processing 38,113-125,1994.
[8] Preteux, F, “On a distance function approach for gray-level mathematical morphology”, In Mathematical Morphology in Image Processing, (E. R. Dougherty, Ed.), Marcel Dekker, New York, ch. 10, pp. 323-349,1993.
[9] Najman, L., and Schmitt, “M. Watershed of a continuous function”, Signal Processing 38, 99-112, 1994.

Keywords
Retina, watershed segmentation, histogram.