Abstract
Retinal image scrutiny is essential to detect and supervise a wide variety of retinal infections. Segmentation of region of interest (ROI) from the retinal image is widely preferred to have a clear idea about the infected section. In the proposed work, a new two-stage approach is presented for automatic segmentation of the optic disc (OD) in retinal images. This approach includes the chaotic bat algorithm (CBA) assisted Kapur’s multi-thresholding as the preprocessing stage and active contour (AC) segmentation as the post-processing stage. This method initially identifies the suitable value of threshold to enhance the OD in the chosen retinal image. The enhanced OD is then processed using the gray scale morphological operation, and finally, the OD is extracted using AC segmentation process. To test the proposed approach, optic disc images of different category are acquired from the RIM-ONE database. Experimental results demonstrate that the average Jaccard index, Dice coefficient, precision, sensitivity, specificity, and accuracy are greater than 83.74, 93.66, 98.18, 92.85, 98.43, and 97.28%, respectively. Hence, the proposed work is extremely significant for the segmentation of OD and can be used as the automated screening tool for the OD related retinal diseases.
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Shriranjani, D., Tebby, S.G., Satapathy, S.C., Dey, N., Rajinikanth, V. (2018). Kapur’s Entropy and Active Contour-Based Segmentation and Analysis of Retinal Optic Disc. In: Nandi, A., Sujatha, N., Menaka, R., Alex, J. (eds) Computational Signal Processing and Analysis. Lecture Notes in Electrical Engineering, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-10-8354-9_26
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