Feature selection and classification for automatic detection of retinal nerve fibre layer thinning in retinal fundus images
by Medha V. Wyawahare; Pradeep M. Patil
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 19, No. 3, 2015

Abstract: Glaucoma which is a leading cause of blindness in the world is not a single disease but a group of disorders with diverse clinical manifestations. If not treated at an early stage it leads to loss of vision. Careful evaluation of optic nerve head structure and Retinal Nerve Fibre Layer (RNFL) is extremely important for diagnosis of the disease and subsequent medication. This work focuses on automatic detection of RNFL thinning, an early indication of glaucoma. A texture-based method is proposed in this work. Texture features were extracted from gray-level co-occurrence matrix and the feature set was optimised using Analysis of Variance (ANOVA) method. Radial basis function classifier was used to discriminate between normal and abnormal regions. The classification results were promising with accuracy of 96.41%.

Online publication date: Wed, 11-Nov-2015

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