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An Efficient Edge Detection Algorithm for Noisy Medical Images

Edge detection is an important technology in the image processing field, particularly in the edges detection and edges extraction. It is the ability to determine the edge of an object, is a primary step in many image enhancement procedures. The edge detection of medical images is existing since years but it is still challenging and scope of research. It has been found that the previous used algorithms were not able to produce optimized or ideal results in different cases. In most 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. In this paper, we propose a new efficient algorithm for edge detection of noisy medical images based on hyper Entropy. Hyper Entropy is defined in order to suppress noise and adapt to different edge in the image. The performance of proposed algorithm is compared against other methods using images corrupted with various levels of "salt &pepper".