A robust edge detection technique based on Matching Pursuit algorithm for natural and medical images

Edge detection has been used widely in image processing, image recognition, and computer vision. Several edge detection techniques have been suggested over time; however, each has a lot of disadvantages. Our method has some steps. At first, the image should be converted into a grayscale image. A 3*3pattern grid is used for producing a discrete vector for any pixel in the present study. Secondly, the length of the pixel vector is increased to linearize and expand. Thirdly, the Matching Pursuit algorithm is applied to the image, and the residual part of the Matching Pursuit algorithm is extracted. Finally, two Hysteresis thresholds have been used for detecting edge pixels. The edge detection problem has been converted into a signal processing problem, so it will be less time-consuming. Moreover, the proposed algorithm is insensitive to noise, and this method can detect weak edge pixels. Also, the performance of the proposed algorithm is compared with three known edge detection algorithms, SOBEL, Laplacian, Canny, and CASENet and the results show that we have a high value of performance parameters such as PR, PSNR, and FOM compared to other algorithms. Furthermore, it is very an effective and reliable method to diagnose and treat diseases in medical images.