Abstract
This paper advocates the use of shape descriptors based on moments of 3D coordinates for morphometry of the cortical sulci. These descriptors, which have been introduced more than a decade ago, are invariant relatively to rotations, symmetry and scale and can be computed for any topology. A rapid insight of the derivation of these invariants is proposed first. Then, their potential to characterize shapes is shown from a principal component analysis of the 12 first invariants computed for 12 different deep brain structures manually drawn from 7 different brains. Finally, these invariants are used to find some correlates of handedness among the shapes of 116 different cortical sulci automatically identified in 144 brains of the ICBM database.
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Mangin, J.F. et al. (2003). 3D Moment Invariant Based Morphometry. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39903-2_62
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DOI: https://doi.org/10.1007/978-3-540-39903-2_62
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