Segmentation and Classification of Image Abnormalities in Retinal Fundus using Discrete Wavelet Transforms
V.Devi1, D.Ravikumar2
1Dr.V.Devi, Department of Computer Applications, Gurunanak College,Chennai, India.
2Dr.D.Ravikumar*, Department of Electronics and Communication Engineering, Vels Institute of Science Technology & Advanced Studies, Chennai, India. 

Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11357-11360 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5412118419/2019©BEIESP | DOI: 10.35940/ijrte.D5412.118419

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Abstract: Glaucoma disease diagnosis greatly based on the accurate retinal image segmentation and classification of images. Segmentation means to divide the images into a patchwork of regions, each of which is “homogeneous”, that is the “same” in some sense. Using discrete wavelet transform, the segmented images are classified by Support Vector Machine (SVM) classifiers to classify the Glaucoma images.The proposed Support Vector Machine classifier is used to extract the information rely on the Region of Interest (ROI) from original retinal fundus image. Thus the classification result are used to find the normal and abnormal image and also to compute the normal and abnormal accuracies.We observed an accuracy of around 93% using data set by SVM classifier.
Keywords: Biomedical Optical Image, Glaucoma, Feature extraction, Fuzzy C-means (FCM), Discrete Wavelet Transform (DWT), Gaussian Filter, Support Vector Machine (SVM).
Scope of the Article: Optical Communication.