3D moment invariant based morphometry

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.