The first step in Active Shape Model (ASM) based image segmentation and processing is to create a point distribution model (PDM) during the training phase. Correct point (landmark) correspondences across each of the training shapes must be determined for a successful and effective statistical model building process. Effective and automatic solutions for this problem are needed for the practical use of ASM methods. In this paper, we provide a solution for this problem which consists of: (i) a process of generating a mean shape without requiring landmarks, (ii) a process of automatic landmark selection for the mean shape, and (iii) a process of propagating landmarks on to each training shape for defining landmarks in them. This paper describes the method of generating the mean shape, and the landmark selection and correspondence process. Although the method is generally applicable to spaces of any dimensionality, our first implementation and evaluation has been carried out for 2D shapes. The method is evaluated on 20 MRI foot data sets, the object of interest being the talus bone. The results indicate that, for the same given number of points, better compactness (number of parameters) of the ASM by using our method can be achieved than by using the commonly used equi-spaced point selection method.