SLP Header

Detection of Optic Disc using Level Set Algorithm

IJCSEC Front Page

Abstract
Glaucoma is an eye disease which damages the optic nerve in the eye. It is the world second leading disease which causes blindness. It can be diagnosed through measurement of optic disc area. Fluid pressure within the eye rises which damages the optic nerve in the eye. Automatic calculation of optic disc boundary is challenging due to the interweavement of blood vessels with surrounding tissues around the disc. A variable level set algorithm of optic disc ellipse optimization improves the accuracy of boundary. The algorithm used is demonstrated on various data sets collected from Aravind Eye hospital from Pondicherry. The area of the optic disc obtained is better than the results of other data sets. This further leads to a large clinical evaluation of the algorithm involving various data sets.
I.Introduction
Glaucoma is one of the common causes of blindness. It causes progressive degeneration of optic nerve fibers and leads to structural changes of the optic nerve head and a simultaneous functional failure of the visual field. Since, glaucoma cannot be cured at final stage and leads to vision loss and cannot be restored, therefore its early detection and subsequent medical treatment is essential to prevent visual damage. Glaucoma is a condition that involves distinctive changes in the optic nerve and visual field. Optic nerve damage in the eye can ordinarily be alleviated and inhibited by sufficiently reducing intraocular pressure (IOP).Glaucoma is a disease of the major nerve of vision, called the optic nerve. The optic nerve receives lightgenerated nerve impulses from the retina and transmits to the brain, where we recognize these electrical signals as vision. If glaucoma is not diagnosed and treated, it leads to loss of vision. Glaucoma is the leading cause of blindness worldwide. In fact, as many as 6 million individuals are blind in both eyes from this disease. A watery material called aqueous humor is present in the eye. The aqueous humor is produced by the ciliary body and is drained through the Canal of Schlemm. If the aqueous humor does not drain out correctly, then pressure will build up in the eye. 3 million people in the United States have been affected by Glaucoma. Half of the people living do not know as such they are affected by the disease. The reason why people are unaware is, they do not know any symptoms and finally results in
RELATED WORKS:
Tracking and detection of retinal blood vessels in fundus images using a novel automated method is presented. A feature vector was computed utilizing multi scale analysis based on Gabor filters for every pixel in an image [1]. [3]A new templatebased methodology for segmenting the OD from digital retinal images is presented. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation. [5]The detection of the optic disc and macula for calculating the diameter of the optic disc and the distance between the optic disc and macula has been explained by using simple statistical techniques. 97% of the optic discs and macula is detected and located, and measured over 94% of the optic discs accurately. [6] An automatic CDR determination method using a variable level-set approach to segment the optic disc and cup from retinal fundus images is proposed. The method is a core factor of ARGALI, a scheme for programmed glaucoma risk consideration [7]. A system to detect OD and cup limit to get relevant disk restriction for glaucoma detection is presented. In general, the cup deformation is not uniform and the sector where the deformation occurs is also used by experts for glaucoma detection. The ellipse fitting strategy followed by the current methods to obtain the CDR is inadequate for this task. [11] A new method for the detection of glaucoma using fundus image which mainly affects the optic disc by increasing the cup size is proposed in paper.[13] Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening.

References:

  1. AlirezaOsareh,“An Automated Tracking Approach for Extraction of Retinal Vasculature in Fundus Images”, Journal of Ophthalmic and Vision Research, vol.5, no.1, 2010.
  2. Chih-Yin Ho,Tun Wen Pai, Hao-Teng Chang, Hsin-Yi Chen, “An atomatic fundus image analysis system for clinical diagnosis of glaucoma” 2011 International Conference on Complex, Intelligent, and Software Intensive Systems pp(559- 564).
  3. Arturo Aquino,“Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques”, IEEE Transactions on Medical Imaging, vol.29, no.11, November 2010.
  4. Bouma, B 2001.“Handbook of Optical Coherence Tomography.” 1stEdn. Dekker, New York.
  5. CemalKöse,“Statistical Techniques for Detection of Optic Disc and Maculaand Parameters Measurement in Retinal Fundus Images”, Journal of Medical and Biological Engineering, vol.31, no 6, pp 395-404, 2011.Cup Segmentation for Glaucoma Screening”, IEEE transactions on medical imaging, vol. 32, no. 6, june 2013.
  6. D. W. K. Wong, “Level-Set Based Automatic Cup-To-Disc Ratio Determination Using Retinal Fundus Images in Argali” , 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008.
  7. GopalDatt Joshi, “Optic Disk and Cup Boundary Detection Using Regional Information”, IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010 .
  8. http://en.wikipedia.org/wiki/Fundus_photography.
  9. T.Walter and J.C. Klein, “Automatic analysis of colour fundus photography and its application to the diagnosis of diabetic retinopathy”, In Handbook of biomedical image analysis, Vol.2, pp 315-368, 2005.
  10. Chih-Yin Ho,Tun Wen Pai, Hao-Teng Chang, Hsin-Yi Chen, “An atomatic fundus image analysis system for clinical diagnosis of glaucoma”, International Conference on Complex, Intelligent, and Software Intensive Systems, pp.559-564,2011
  11. JagadishNayak , Rajendra Acharya U. P SubbannaBhat, Nakulshetty, Teik-cheng Lim “Automated Diagnosis of Glaucoma Using Digital Fundus Images” , Journal of Medical systems ,2009.
  12. Quigley, H. A., Broman, A. T., “The number of people with glaucoma worldwide in 2010 and 2020,” British Journal of Ophthalmology (2006); 90:262.
  13. Jun Cheng*, Jiang Liu, YanwuXu, Fengshou Yin, Damon Wing Kee Wong, Ngan-Meng Tan, DachengTao,Ching-Yu Cheng, Tin Aung, and Tien Yin Wong,” Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening”,2013.