IJRCS – Volume 4 Issue 3 Paper 4

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OPTIMIZED AND EFFICIENT DIAGNOSIS OF GLAUCOMA BASED ON WAVELET FEATURE EXTRACTION AND CLASSIFICATION

Author’s Name : R Kamalin Sheeba | R Sujitha

Volume 04 Issue 03  Year 2017  ISSN No:  2349-3828  Page no:  13-17

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Abstract:

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. It damages the optic nerve subsequently causes loss of vision. The methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. The available scanning methods are Heidelberg Retinal Tomography(HRT), Scanning Laser Polarimetry(SLP) and Optical Coherance Tomography(OCT ). The features are considered from the complete fundus or a sub image of the fundus, it is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.

Keywords:

Glaucoma, Segmentation, In-painting, Feature Extraction

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