SLP Header

An Efficacious Method of Cup to Disc Ratio Calculation for Glaucoma Diagnosis Using Super pixel

IJCSEC Front Page

Abstract
Glaucoma is a chronic eye disease that leads to vision loss. Since it cannot be cured, detecting the disease in time is important. The tests which are done to detect Glaucoma using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. The assessment of Optic nerve head damage in retinal fundus images is both more promising and superior. In this paper the optic disc and optic cup segmentation using Superpixel classification for glaucoma screening. The SLIC (Simple Linear Iterative Clustering) algorithm is incorporated to segment the fundus retinal image into compact and nearly uniform superpixels. Unlike dividing an image into a grid of regular pixels, superpixels have the important property of preserving local boundaries. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier to confirm Glaucoma for a given patient. Superpixels are becoming increasingly popular in computer vision applications because of improved performance over pixel-based methods.
Keywords:intraocular pressure (IOP), superpixel, cup to disc ratio (CDR).
I.Introduction
Glaucoma is a group of eye diseases characterized by damage to the optic nerve. It is an eye disease which causes irreversible loss of vision. In its early stages, glaucoma may present few or no symptoms and can gradually steal sight. In fact, most people affected by Glaucoma do not know if they have the disease or not. If left undetected and untreated, glaucoma can lead to blindness. One of the high risk factors for glaucoma is elevated Intraocular pressure (IOP), or pressure inside the eye. A healthy and a normal eye secrete a fluid named aqueous humor, at the same rate at which it drains. The pressure is increased when the draining system is blocked and the fluid cannot exit at a normal rate. This increased IOP pushes against the optic nerve causing gradual damage, which may result in vision loss, usually starting with the peripheral, or side vision.
Increased eye pressure is often associated with gradual damage to the nerve fibers that make up the optic nerve. In this paper optic disc and optic cup are segmented using superpixel classification for detecting Glaucoma. For automatic optic nerve head assessment we can use the image features for a binary classification between glaucomatous and healthy subjects. These features are normally computed at the imagelevel. CDR is commonly used because of its accuracy and simplicity. When CDR is greater than 0.65,then it indicates a high risk of glaucoma [10]. However, because 3-D images are not easily available, 2-D color fundus images are still referred to by most clinicians. Moreover, the high cost of obtaining, 3-D images make it inappropriate for a large-scale screening program. This paper focuses on automatic glaucoma screening using CDR from 2-D fundus images. The CDR is computed as the ratio of the vertical cup diameter (VCD) to vertical disc diameter (VDD) clinically.
PROBLEM STATEMENT:
An early detection of glaucoma is particularly significant since it allows timely treatment to prevent major visual field loss and prolongs the effective years of usable vision. The diagnosis of glaucoma can be done through measurement of CDR (cup-to-disc ratio). Currently, CDR evaluation is manually performed by trained ophthalmologists or expensive equipment such as Heidelberg Retinal Tomography (HRT). However, CDR evaluation by an ophthalmologist is subjective and the availability of HRT is very limited. Thus, this paper proposes an intuitive, efficient and objective method for automatically classifying digital fundus images into either normal or glaucomatous types in order to facilitate ophthalmologists.

References:

  1. R. Achanta, A. Shaji, K. Smith, A. Lucchi and P. Fua et al. SLIC Superpixels Compared to State-of-the-art Superpixel Methods, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274 - 2282, 2012.
  2. P. Felzenszwalb and D. Huttenlocher, “Efficient Graph- Based Image Segmentation,” Int’l J. Computer Vision, vol. 59, no. 2, pp. 167-181, Sept. 2004.
  3. A. Fitzgibbon, M. Pilu and R. B. Fisher, Direct least-squares fitting of Ellipses, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 476-480, 1999
  4. S. Kavitha, S. Karthikeyanand Dr. Duraiswamy, Early Detection of Glaucoma in Retinal Images Using Cup to Disc Ratio, Computing Communication and Networking Technologies (ICCCNT), pp. 1-5, July 2010
  5. A. Levinshtein, A. Stere, K. Kutulakos, D. Fleet, S. Dickinson, and K Siddiqi, “Turbopixels: Fast Superpixels Using Geometric Flows,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2290-2297, Dec. 2009.
  6. D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics,” Proc. IEEE Int’l Conf. Computer Vision, July 2001.
  7. X. Ren and J. Malik. Learning a Classification Model for Segmentation. In Proc. International Conference on Computer Vision, pages 10–17, 2003.
  8. L. G. Shapiro and G. C. Stockman, Computer Vision.: Prentice Hall, 2001.
  9. J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
  10. Singapore Ministry of Health (October,2005). Glaucoma Clinical Practice Guideline [Online].Available: http://www.moh.gov.sg/mohcorp/publications
  11. A.Vedaldi and S. Soatto, “Quick Shift and Kernel Methods for Mode Seeking,” Proc. European Conf. Computer Vision, 2008.
  12. O. Veksler, Y. Boykov, and P. Mehrani, “Superpixels and Supervoxels in an Energy Optimization Framework,” Proc. European Conf. Computer Vision, 2010.
  13. Wang, M.Y., Maurer, C.R., Jr., Fitzpatrick, J.M. and Maciunas, R.J., ”An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head”, IEEE Transactions on Biomedical Engineering, pp. 627-637, August 2002.
  14. Prasanalakshmi. B and Kannammal. A "Secure Cryptosystem from Palm Vein Biometrics in Smart Card" The 2nd International Conference on Computer and Automation Engineering (ICCAE), IEEE Publisher. Feb 26-28,2010. Volume:1,p 653-957