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12 March 2010 Structural template formation with discovery of subclasses
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Abstract
A major focus of computational anatomy is to extract the most relevant information to identify and characterize anatomical variability within a group of subjects as well as between different groups. The construction of atlases is central to this effort. An atlas is a deterministic or probabilistic model with intensity variance, structural, functional or biochemical information over a population. To date most algorithms to construct atlases have been based on a single subject assuming that the population is best described by a single atlas. However, we believe that in a population with a wide range of subjects multiple atlases may be more representative since they reveal the anatomical differences and similarities within the group. In this work, we propose to use the K-means clustering algorithm to partition a set of images into several subclasses, based on a joint distance which is composed of a distance quantifying the deformation between images and a dissimilarity measured from the registration residual. During clustering, the spatial transformations are averaged rather than images to form cluster centers, to ensure a crisp reference. At the end of this algorithm, the updated centers of the k clusters are our atlases. We demonstrate this algorithm on a subset of a public available database with whole brain volumes of subjects aged 18-96 years. The atlases constructed by this method capture the significant structural differences across the group.
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Xiaojing Long and Chris Wyatt "Structural template formation with discovery of subclasses", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76231B (12 March 2010); https://doi.org/10.1117/12.843994
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