Euclidean Distance transformation and watershed are fundamental technique for the application fields of image understanding and computer vision. Calculated using the watershed transformation can be extracted important image features. Such as the identification of the contour and the number of such applications. In this paper, we will compare the effects of our proposed watershed transform method and other watershed transformation method. Our proposed method will be more accurate. In image processing, object boundary segmentation is an important and fundamental issue.This study modified the traditional style of the watershed transformation,and we proposed the concept of an election-style, so that contact between the object image can be properly divided. By this method, image of objects can be converted according to the results of Euclidean distance transformation. It is easier to obtain images of various objects in the correct profile for easy application in medical image. Finally,we compare our methed with the National Institutes of Health (NIH) developed image processing software ImageJ, and marker-controlled watershed transformation of other scholars.

Euclidean Distance transformation is a fundamental technique for the application fields of image understanding and computer vision. Some important characteristics in image analysis such as shape factor, skeleton and medial axis are based upon the distance transformation computation. The lookup table algorithm is based upon the recursive computation structure of the 4N method. Therefore, this algorithm is very fast and is close to the 4N method, which performs as the fastest one among all the comparing algorithms in our experiments. The success of the lookup table algorithm is based upon a checking strategy by error geometry. The error candidates are arranged in order according to their distances to the reference point. In addition, a Local_Array is used to store the y coordinates of the closest foreground pixels above the processing line. Therefore we can find the correct feature point by checking the ordered candidates with the information provided from the Local_Array instead of comparisons among the candidates. In contrast, all the comparing eror-free Euclidean algorithms select their feature points from candidates by time consuming distance comparison.