Edge detection is an important part of image processing that helps in classifying and finding gaps or break in an image. It also helps recognizing points in digital image where a sharp change in brightness occurs. These points represent curves. In image, these curves also called edges. The quality of edge detection determined by performance of the successive steps performed. Edge detection is very difficult work in automated image analysis as it requires to process large data to extract knowledge. There are no specific techniques for edge detection that works well for all type of images. The objective of this research is to analyze and design a technique in conjunction with the Canny edge detector to better edge detection rate by minimizing the influence of external noise which ultimately leads to detection of false edges. First, we used RGB image as input image and convert this into grayscale image. Second, we convolved the image by gaussian filter. Third, we used Sobel operator to find the gradient of image and then calculate gradient magnitude and direction. Fourth, we used non-maximum suppression to thin the edges. And last we used two threshold values to find the true edges in image and get the edge detected image. The performance of the proposed method is compared with Canny, Roberts, Sobel, and Prewitts by using objective quality assessment metrices including PSNR, SSIM, RMSE, EI, MI, SD, and Variance. The results show that the proposed method is more efficient in detecting edges as compared to other algorithms in the same domain.