Retinal Blood Vessel Segmentation using Hybrid Optimization and Support Vector Machine Classification
Rashmi Deep1, Nitika Kapoor2
1Rashmi Deep, Department of Computer Science & Engineering, Chandigarh University, Mohali, Punjab, India.
2Nitika Kapoor, Department of Computer Science & Engineering, Chandigarh University, Mohali, Punjab, India.
Manuscript received on 01 April 2019 | Revised Manuscript received on 07 May 2019 | Manuscript published on 30 May 2019 | PP: 1787-1792 | Volume-8 Issue-1, May 2019 | Retrieval Number: A2118058119/19©BEIESP
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Abstract: The detection and diagnosis of the various diseases at initial phase observer feel some changes in the structures of RBVs (retinal blood vessels). Segmentation of the retina blood vessels is required because main phase for the discovery of the alterations present in features of the RBVs images. In this research, the color digital retinal image has been proposed as the segmentation technique of the blood vessel. Diabetic retinopathy (DR) is the method where the automated removal and the variety of diseases are classified. The extraction of the blood vessel segmentation from RBV s through fundus imageries is the main issue. Hence, in this research, the simple and the automatic method has been proposed for the extraction of the retinal blood vessels. In the proposed approach, the DIP (digital image processing method) used for the removal and the organization of the retrieval of the noise, hybrid ant lion optimization (ALO) method and the change in the contrast of the image. In the proposed approach the group of the retinal images has been tested. The collection of the retinal images from the DRIVE dataset and evaluate the correctness by the performance analysis. The experimental results describe the parameters which are exactness, specificity, sensitivity and the scoring method.
Index Terms: Blood Vessel, Segmentation, Hybrid (ALO) Optimization, DRIVE Database.

Scope of the Article: Classification