Euclidean distance transformation is a fundamental technique in image understanding and computer vision. Some important characteristics in image analysis such as skeleton and object boundary are based upon the distance transformation computation. In this thesis, we compare our method of computing Euclidean distance transformations with the method of Chamfer distance transformation. Our method is faster and more accurate than the Chamfer method. The boundary detection is an interesting and challenging task in computer vision. We integrate distance transform, watershed transform and active contour model to achieve boundary detection. Our method can successfully separate the touching objects, so as to facilitate the subsequent image processing for obtaining the geometric, and texture characteristics of objects. These features are useful for further medical images applications.