Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

Abstract

Medical image processing has become one of the crucial elements of the diagnostic process because of the increased usage of medical imaging recently, and clinicians' dependence on such computer-processed medical images in diagnosing patients. As the traditional Canny edge detection algorithm is sensitive to noise, it is easy to lose weak edge information when filtering out the noise, and its fixed parameters show poor adaptability. The suggested algorithm introduced the concept of image block intensity operator to replace image gradient. In addition, the computing speed of the suggested algorithm is relatively fast because it works block by block rather than pixel by pixel. Two adaptive threshold selection methods are presented, one based on the median accumulative histogram of image gradient magnitude and the other on the standard deviation for both types of image pixels (one with less edge information and the other with rich edge information). The proposed algorithm can be dividing into four stages: Input the medical digital image, convert the color medical image to gray-scale, applied improved canny edge detection, then calculate the MSE & PSNR Measures, in addition conduct a visual questionnaire by oncologists to find out which method that made the enhancement of the medical image clearer.