earticle

영어

Medical image edge detection is an important work for object recognition of the human organs, and it is an essential pre-processing step in medical image segmentation and 3D reconstruction. Although many edge-detection evaluation methods have been developed in the past years, however this is still a challenging and unsolved problem. Conventionally, edge is detected according to some early brought forward algorithms like Canny, LOG, Sobel, Prewitt, Roberts algorithms but in theory they belong to the high pass filtering, which are not fit for noise medical image edge detection because noise and edge belong to the scope of high frequency. In real world applications, medical images contain object boundaries and object shadows and noise. Therefore, they may be difficult to distinguish the exact edge from noise or trivial geometric features. After studying all traditional methods of edge detection, it has been analyzed that for these situations, a new algorithm is needed which is optimal. In this paper, we propose a new algorithm for edge detection of noisy medical images based on both Tsallis and Shannon entropy together. The performance of our method is compared against other methods by using blood cells image corrupted with various levels of "salt and pepper". It is observed that the proposed algorithm displayed superior noise resilience and decrease the computation time.