An analysis of CANNY and LAPLACIAN of GAUSSIAN image filters in regard to evaluating retinal image

Abstract

This paper deals with the analysis of performance of Canny and Laplacian of Gaussian filter in edge detection of retinal images. Edge detection is one of the methods in image segmentation in Image Processing. Classical methods of edge detection involve convolving the image with an operator (a 2-D filter), which is constructed to be geometry of the operator which determines a characteristic direction in which it is most sensitive to edges. It is important to have efficient edge detection technique. In this paper, comparative analysis of the aforesaid filters is done and found that Canny edge operator performs better than Laplacian of Gaussian filter in most of the varieties of retinal images under various conditions. Here in this paper, we pertain only with human retinal images under diverse conditions. We have shown healthy retina, retina blood vessels, disease affected retina, optic disc etc.

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“…The Log edge detection filters the image with a Laplacian of Gaussian filter (LoG), hence the name. The Gaussian filter is useful to smooth the random noise in the image, which is also another advantage in the Canny algorithm [40]. The results of the application of these algorithms are shown in Figure 6.…”

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“…Different edge detector approaches have also been used to deal with the issue, including specific improvements since they normally present certain draw-backs in the detection of vessels in noisy scenes or blurred contours. Dhar et al [8] studied the robustness of the Canny and Laplacian of Gaussian detectors, demonstrating that the Canny filter offers a more stable behavior in a variety of image conditions. Xiaolin et al [37] modified Canny with a bilateral filter to remove noisy artifacts and facilitate vessel detection.…”