Groupwise morphometric analysis based on high dimensional clustering

Dong Hye Ye, Kilian M. Pohl, Harold Litt, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose an efficient groupwise morphometric analysis to characterize morphological variations between healthy and pathological states. The proposed framework extends the work of Baloch [4] in which a manifold for each anatomy was constructed by collecting lossless [transformation, residual] descriptors with various transformation parameters, and the optimal set of transformation parameters was estimated individually by minimizing group variance. However, full parameter exploration is not desired as it can result in transformation leading to inaccurate anatomical models. In addition, a single fixed template introduces a priori bias to subsequent statistical analysis. In order to overcome these limitations, we use an affinity propagation clustering method to find the spatially close cluster center for each subject. Then, a subject is normalized to the template via the cluster center to restrict our analysis only to those descriptors that reflect reasonable warps. In addition, a mean template is selected by finding a cluster center that minimizes the sum of pairwise shape distance to reduce the fixed template bias. Our method is applied to 2D synthetic data and 3D real Cardiac MR Images. Experimental results show improvement in quantifying and localizing shape changes.

Original languageEnglish (US)
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
Pages47-54
Number of pages8
DOIs
StatePublished - 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 - San Francisco, CA, United States
Duration: Jun 13 2010Jun 18 2010

Publication series

Name2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

Other

Other2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
CountryUnited States
CitySan Francisco, CA
Period6/13/106/18/10

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Ye, D. H., Pohl, K. M., Litt, H., & Davatzikos, C. (2010). Groupwise morphometric analysis based on high dimensional clustering. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 (pp. 47-54). [5543438] (2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010). https://doi.org/10.1109/CVPRW.2010.5543438