OPTIMIZED RETINAL BLOOD VESSEL SEGMENTATION TECHNIQUE FOR DETECTION OF DIABETIC RETINOPATHY

Sukhpreet Kaur, Kulvinder Singh Mann

Abstract


Most of the retinal diseases namely retinopathy, occlusion etc., can be identified through changes exhibited in retinal vasculature of gathered images. Thus, segmentation of retinal blood vessels aids in detecting the alterations and hence the disease. In one data set there are large numbers of features but not all are useful in classification. The useless redundant features reduce the performance of output. In our case we have used only small relevant features rather than using all the features that contain both relevant and irrelevant features. To achieve better results there is need to minimize the number of features along with maximizing the performance of classification. In this paper, to achieve the objective firstly we have extracted the features using the Principle component analysis (PCA) for both series of vessels and non vessel images (normal and abnormal). Then further extracted features are optimized using various optimization methods which actually optimize the selected features of images taken from DRIVE dataset for both vessels and non-vessels to improve the recognition accuracy. Afterwards, the naive bayes classifier has been applied to test the results for both normal and abnormal images dataset. The accuracy of PSO optimization is 0.99537 which is 31.42% better than LION and 53.09% better than Firefly. The simulation results show that PSO is better in terms of accuracy, sensitivity, entropy, etc., as compared to other optimization methods for both normal and abnormal set of images.

Keywords


Vessel images, Non vessel images, Particle Swarm Optimization (PSO), Firefly, LION, Accuracy, Sensitivity

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DOI: https://doi.org/10.26483/ijarcs.v8i9.5071

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