Gauss Gradient Algorithm for Edge Detection in Retinal Optical Coherence Tomography Images

Medical imaging modalities play a vital role in the healthcare sector for disease diagnosis and prediction. Optical Coherence Tomography uses near-infrared rays for generating cross-sectional images of the retina. Edge detection is a traditionalROI extractionalgorithm thatextracts the boundary of objects in an image. This research work focuses on the gauss gradient-based edge detection model for boundary detection in Optical Coherence Tomography images of the retina. The separable feature of a 2D Gaussian kernel is used, and a 1D kernel for the x and y directions is created. The Gaussian kernel utilized in this research work is the convolution result of Gaussian function and first-order derivative of Gaussian function. For performance validation, the Berkeley segmentation data set was utilized, when compared to traditional edge detection models, and better results were obtained for the gauss gradient algorithm.