Multiple Atlas Inference and Population Analysis Using Spectral Clustering

In medical imaging, constructing an atlas and bringing an image set in a single common reference frame may easily lead the analysis to erroneous conclusions, especially when the population under study is heterogeneous. In this paper, we propose a framework based on spectral clustering that is capabl...

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Bibliographic Details
Published in2010 20th International Conference on Pattern Recognition pp. 2500 - 2503
Main Authors Sfikas, G, Heinrich, C, Nikou, C
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
atlas inference
Biomedical imaging
Brain
Clustering algorithms
Computational modeling
Laplace equations
spectral clustering
Training
Training data
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Summary:In medical imaging, constructing an atlas and bringing an image set in a single common reference frame may easily lead the analysis to erroneous conclusions, especially when the population under study is heterogeneous. In this paper, we propose a framework based on spectral clustering that is capable of partitioning an image population into sets that require a separate atlas, and identifying the most suitable templates to be used as coordinate reference frames. The spectral analysis step relies on pairwise distances that express anatomical differences between subjects as a function of the diffeomorphic warp required to match the one subject onto the other, plus residual information. The methodology is validated numerically on artificial and medical imaging data.
ISBN:1424475422
9781424475421
ISSN:1051-4651
DOI:10.1109/ICPR.2010.612