Abstract
In this paper we present a novel technique for non-rigid medical image registration and correspondence finding based on a multiple-layer flexible mesh template matching technique. A statistical anatomical model is built in the form of a tetrahedral mesh, which incorporates both shape and density properties of the anatomical structure. After the affine transformation and global deformation of the model are computed by optimizing an energy function, a multiple-layer flexible mesh template matching is applied to find the vertex correspondence and achieve local deformation. The multiple-layer structure of the template can be used to describe different scale of anatomical features; furthermore, the template matching is flexible which makes the correspondence finding robust. A leave-one-out validation has been conducted to demonstrate the effectiveness and accuracy of our method.
Original language | English (US) |
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Pages (from-to) | 1145-1165 |
Number of pages | 21 |
Journal | International Journal of Pattern Recognition and Artificial Intelligence |
Volume | 17 |
Issue number | 7 |
DOIs | |
State | Published - Nov 2003 |
Keywords
- Correspondence
- Multiple-layer flexible mesh template
- Non-rigid registration
- Statistical model
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Artificial Intelligence