Statistical shape model (SSM) is an efficient method of image processing and analysis. One key factor in building models is to obtain correspondent landmarks among the whole shape dataset. Traditional manual land-marking is temporally expensive

subjective，boring and prohibitively extensive in dimension. In this paper

a multi-scale parameterization on shapes allows a minimum description length (MDL) based optimization on landmark correspondence in a coarse scale and a most convenient arc parameterization based landmark correspondence in a fine scale. This achieves a fast and accurate SSM building

which is the foundation on following image segmentation and quantitative analysis. In experiments

SSMs are built with vertebral body

intervertebral disc and meniscus shapes extracted from various MRIs respectively. It is testified that the models built with the proposed scheme is not only more repeatable and concise than model baseds on manual landmarking

but also more temporally efficient than model purely based on optimization. The segmentation errors from the proposed method are comparable with or better than those from the manual modeling based segmentation.