Abstract
Many medical image analysis problems that involve multi-modal images lend themselves to solutions that involve class posterior density function images. This paper presents a method for large deformation exemplar class posterior density template estimation. This method generates a representative anatomical template from an arbitrary number of topologically similar multi-modal image sets using large deformation minimum Kullback-Leibler divergence registration. The template that we generate is the class posterior that requires the least amount of deformation energy to be transformed into every class posterior density (each characterizing a multi-modal image set). This method is computationally practical; computation times grows linearly with the number of image sets. Template estimation results are presented for a set of five 3D class posterior images representing structures of the human brain.
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Keywords
- Mutual Information
- Image Registration
- Multimodal Image
- Computational Anatomy
- Multimodal Image Registration
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Lorenzen, P., Davis, B., Gerig, G., Bullitt, E., Joshi, S. (2004). Multi-class Posterior Atlas Formation via Unbiased Kullback-Leibler Template Estimation. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_12
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DOI: https://doi.org/10.1007/978-3-540-30135-6_12
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