Comparisons of Different Methods of Image Edge Detection With Morphologic Method

Edges define the boundaries between regions in an image, which helps with segmentation and object recognition. The usual existing edge detection methods like LoG, Canny, Sobel, Prewitt etc. They make use of maximal gradient value or zero-crossing point of secondorder derivatives operator or appropriate threshold value obtain image edge. They all belong to high pass filter and unsuitable to detect the noise corruption of the medical image, because noise and edge are located in high frequency domain.

Morphological edge detectors involve simple addition/subtraction operations and max/min operations. Since different edge detectors work better under different conditions, it would be ideal to have an algorithm that makes use of multiple edge detectors, applying each one when the scene conditions are most ideal for its method of detection. In order to create this system, it is first required to know which edge detectors perform better under which conditions. Clear edges can show where shadows fall in an image or any other distinct change in the intensity of an image. The process of extraction of these feature points is called edge detection.