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Evaluation of 3D Correspondence Methods for Model Building

M. A. Styner, K. T. Rajamani, L. P. Nolte, G. Zsemlye, G. Székely, Ch. J. Taylor and R. H. Davies
Information Processing in Medical Imaging


The correspondence problem is of high relevance in the construction and use of statistical models. Statistical models are used for a variety of medical application, e.g. segmentation, registration and shape analysis. In this paper, we present comparative studies in three anatomical structures of four different correspondence establishing methods. The goal in all of the presented studies is a model-based application. We have analyzed both the direct correspondence via manually selected landmarks as well as the properties of the model implied by the correspondences, in regard to compactness, generalization and specificity. The studied methods include a manually initialized subdivision surface (MSS) method and three automatic methods that optimize the object parameterization: SPHARM, MDL and the covariance determinant (DetCov) method. In all studies, DetCov and MDL showed very similar results. The model properties of DetCov and MDL were better than SPHARM and MSS. The results suggest that for modeling purposes the best of the studied correspondence method are MDL and DetCov.

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  author = {M. A. Styner and K. T. Rajamani and L. P. Nolte and G. Zsemlye and G. Székely and Ch. J. Taylor and R. H. Davies},
  title = {Evaluation of 3D Correspondence Methods for Model Building},
  booktitle = {Information Processing in Medical Imaging},
  year = {2003},
  keywords = {}