Retinal Fundus Image Research for Diagnosis of Diabetic Maculopathy
Joshua Thomas1, K.S. Angel Viji2
1Joshua Thomas, Research Scholar, Department of Electronics and Communication Engineering, Noorul Islam University, (Tamil Nadu), India.
2K. S. Angel Viji, Research Supervisor, Department of Computer Science Engineering, Noorul Islam University, (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 339-347 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10530982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1053.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fundus images are valuable resources in diagnosis of retinal diseases. This paper proposes a computer-aided method based on various feature extraction techniques and support vector machines (SVM) for detection and classification of diabetic maculopathy (DM). DM, defined as retinopathy within one disc diameter of the centre of the macula, is a major cause of sight loss in diabetes. Here, we bring out a new approach to detect DM based on retinal fundus image features. During the first stage the input image is enhanced and the optic disc is masked to determine the presence of regions of foveal neighborhood. The second stage, deals with various feature extraction technique based on transform, shape and texture features. Extracted features are further categorized as healthy or affected images. Here we go for classification task using the RBF Support Vector Machine (SVM) classification, the techniques have been tested on retinal databases and these are compared with trained phase to categorize Healthy and DM images. This method can detect DM with a level accuracy on par with human retinal specialists.
Keywords: Diagnosis Image Classification Retinal Resources.
Scope of the Article: Image Processing and Pattern Recognition