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MICCAI 2001 LNCS 2208 24 32 Shape versus Size Improved understanding of the Morphology of Brain Structures 1 2 Guido Gerig 1Martin Styner 3Martha E Shenton 2Jeffrey A Lieberman 1 Department of Computer Science UNC Chapel Hill NC 27599 USA 2 Department of Psychiatry UNC Chapel Hill NC 27599 USA 3 Department of Psychiatry VAMC Brockton Harvard Medical School Boston USA gerig cs unc edu martin styner ieee org Abstract Standard practice in quantitative structural neuroimaging is a segmentation into brain tissue subcortical structures fluid space and lesions followed by volume calculations of gross structures On the other hand it is evident that object characterization by size does only capture one of multiple aspects of a full structural characterization Desirable parameters are local and global parameters like length elongation bending width complexity bumpiness and many more In neuroimaging research there is increasing evidence that shape analysis of brain structures provides new information which is not available by conventional volumetric measurements This motivates development of novel morphometric analysis techniques answering clinical research questions which have been asked for a long time but which remained unanswered due to the lack of appropriate measurement tools Challenges are the choice of biologically meaningful shape representations robustness to noise and small perturbations and the ability to capture the shape properties of populations that represent natural biological shape variation This paper describes experiments with two different shape representation schemes a finescale global surface characterization using spherical harmonics and a coarsely sampled medial representation 3D skeleton Driving applications are the detection of group differences of amhygdala hippocampal shapes in schizophrenia and the analysis of ventricular shape similarity in a mono dizygotic twin study The results clearly demonstrate that shape captures information on structural



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