Comparative Analysis of Image Quality Values on Edge Detection Methods

Abstract

Identification of object boundaries in a digital image is developing rapidly in line with advances in computer technology for image processing. Edge detection becomes important because humans in recognizing the object of an image will pay attention to the edges contained in the image. Edge detection of an image is done because the edge of the object in the image contains very important information, the information obtained can be either size or shape. The edge detection method used in this study is Sobel operator, Prewitt operator, Laplace operator, Laplacian of Gaussian (LoG) operator and Kirsch operator which are compared and analyzed in the five methods. The results of the comparison show that the clear margins are the Sobel, Prewitt and Kirsch operators, with PSNR calculations that produce values ​​above 30 dB. Laplace and LoG operators only have an average PSNR value below 30 dB. Other quality comparisons use the histogram value and the contrast value with the highest value results in the Laplace and LoG operators with an average histogram value of 110 and a contrast value of 24. The lowest histogram and contrast value are owned by the Sobel and Prewitt operators.

Optimization Ground Glass Opacities (GGO) Detection Using Multipixel Interpolation Techniques

“…Edge detection is one of the techniques interested in an image in recognizing objects with edges contained in the image [37]. Edge detection is also a process of forming an object [38].…”