| Title: |
Unbiased group-wise alignment by iterative central tendency estimations |
| Authors: |
De Craene, Mathieu; Macq, Benoît; Marques, Ferran; Salembier, P; Warfield, Simon |
| Contributors: |
UCL - FSA/ELEC - Département d'électricité; UPC Barcelone - Image and Video Processing group; Harvard Medical School (USA) - Computationel radiology Laboratory; UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique |
| Source: |
Mathematical Modelling of Natural Phenomena, Vol. volume 3, no. Issue 6, p. 2-32 (January 2008) |
| Publisher Information: |
V. Volpert # Institute of Mathematics University Lyon 1 |
| Publication Year: |
2008 |
| Collection: |
DIAL@USL-B (Université Saint-Louis, Bruxelles) |
| Description: |
This paper introduces a new approach for the joint alignment of a large collection of segmented images into the same system of coordinates while estimating at the same time an optimal common coordinate system. The atlas resulting from our group-wise alignment algorithm is obtained as the hidden variable of an Expectation-Maximization (EM) estimation. This is achieved by identifying the most consistent label across the collection of images at each voxel in the common frame of coordinates. In an iterative process, each subject is iteratively aligned with the current probabilistic atlas until convergence of the estimated atlas is reached. Two different transformation models are successively applied in the alignment process: an affine transformation model and a dense non-rigid deformation field. The metric for both transformation models is the mutual information that is computed between the probabilistic atlas and each subject. This metric is optimized in the affine alignment step using a gradient based stochastic optimization (SPSA) and with a variational approach to estimate the non-rigid atlas to subject transformations. A first advantage of our method is that the computational cost increases linearly with the number of subjects in the database. This method is therefore particularly suited for a large number of subjects. Another advantage is that, when computing the common coordinate system, the estimation algorithm identifies weights for each subject on the basis of the typicality of the segmentation. This makes the common coordinate system robust to outliers in the population. Several experiments are presented in this paper to validate our atlas construction method on a population of 80 brain images segmented into 4 labels (background, white and gray matters and ventricles). First, the 80 subjects were aligned using affine and dense non-rigid deformation models. The results are visually assessed by examining how the population converges closer to a central tendency when the deformation model allows more degrees ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
boreal:90634; http://hdl.handle.net/2078.1/90634 |
| Availability: |
http://hdl.handle.net/2078.1/90634 |
| Accession Number: |
edsbas.B3EFE6CC |
| Database: |
BASE |