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Improved Edge Detection Methods in OCT Images using a Hybrid Framework based on CGWO Algorithm

Optical Coherence Tomography (OCT) is regarded as a noninvasive technique for diagnostic purposes. It tends to provide a cross-sectional view of OCT images and it represents the structures based on the optical properties of tissues. In order to clearly represent the image structures, it is therefore very essential to improve the edges in OCT images. In this paper, a hybrid mechanism is adopted over to find out the edges. At the initial phase, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for OCT image enhancement. The enhanced image is applied to canny edge detection algorithm. To validate the canny edge method, its performance is compared with the existing edge detectors namely Prewitt, Sobel, Laplacian of Gaussian (Log), and Robert Edge detectors. The performance metrics such as Entropy, Mean-Square-Error (MSE), Signal-to- Noise-Ratio(SNR), Peak-Signal-to-Noise-Ratio(PSNR), Structural Similarity Index Measures (SSIM), Edge Preservation Index (EPI) are evaluated for all these edge detection schemes and observe that canny edge detectors offer better result. In this paper an improved edge detection scheme namely Canny Grey Wolf Optimization (CGWO) is proposed and validated. This hybrid frame work is found to offer better result for edge detection.