SLGP Header

GKFCM Clustering and Classification for Low Intensity Inhomogeneity Glaucomatous Retinal Images

IJEECC Front Page

In the current scenario, the possibilities for the cause occurred in retina create a huge impact in day today nature. The proposed algorithm will bring a heavy monitoring of our cornea part of the body in the initial stage. The Technique follows with the feature Extraction of the Grey scale low intensity images to identifies the smoothen region of the image (640x480). Such that which will be fed as input to the clustering part(GKFCM) Gaussian Kernel Fuzzy C means clustering help in detecting the affected area of the retinal image in a simplified technique using the method call as cup and segment schema extraction, between two perspectives. This implementation of analysis is carried over for collection of images in the database (100 images), as a verification the overall clustered data will be fed as input to the SVM (support vector machine) which helps in identifying the region of affected area in prior, the performance measures paved a high powerful technique compared to the existing methodology, which yields the overall positive ration as nearly to 97% through the collected Database.
Keywords:GKFCM, SVM


  1. HuseyinSeker, Raouf N. G. Naguib “A Fuzzy Logic Based-Method for Prognostic Decision Making in Breast and Prostate Cancers” IEEE,2003.Vol.7,No.2,pp.2-7.
  2. Juan Xu, Member, Hiroshi Ishikawa, “3D Optical Coherence Tomography SuperPixel with Machine Classifier Analysis for Glaucoma Detection” IEEE EMBS International Conference, 2011.
  3. K.Kavitha, S.Arivazhagan, N.Kayalvizhi,”Wavelet Based Spatial - Spectral Hyper spectral Image Classification Technique Using Support Vector Machines”, IEEE International conference, 2010.
  4. Madhusudhanan Balasubramanian, Peter Wolenski,“A Framework for Detecting Glaucomatous Progression in the Optic Nerve Head of an Eye using proper Orthogonal Decomposition”,IEEE,2009. Vol 13, No.5.pp.3-10.
  5. Marcelo Dias, Vanessa Vidotti, “High Definition Optical Coherence Tomography and Standard Automated Perimetry Dataset Generator for Glaucoma Diagnosis”
  6. Sambasiva Rao Baragada, S.Ramakrishna, M.S.Rao, S.Purushothaman, “Implementation of Radial Basis Function Neural Network for Image Steganalysis”, International Journal of Computer Science and Security, Vol 2, No.1,pp. 2-9.
  7. SumeetDua, U. RajendraAcharya, “Wavelet-Based Energy Features for Glaucomatous Image Classification”, IEEE, 2012, Vol 16, No.1, pp.1-8.
  8. U.RajendraAcharya, SumeetDua, ”Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features” IEEE, 2011.Vol 15, No.3, pp.1-7.