Automatic inference of sulcus patterns using 3D moment invariants

Authors:
Z. Y. Sun;D. Rivière;F. Poupon;J. Régis;J.-F. Mangin
Affiliations:
Neurospin, I2BM, CEA, France and IFR 49, France;Neurospin, I2BM, CEA, France and IFR 49, France;Neurospin, I2BM, CEA, France and IFR 49, France;Service de Neurochirurgie Fonctionnelle, CHU La Timone, Marseille, France;Neurospin, I2BM, CEA, France and IFR 49, France
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

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Abstract

The goal of this work is the automatic inference of frequent patterns of the cortical sulci, namely patterns that can be observed only for a subset of the population. The sulci are detected and identified using brainVISA open software. Then, each sulcus is represented by a set of shape descriptors called the 3D moment invariants. Unsupervised agglomerative clustering is performed to define the patterns. A ratio between compactness and contrast among clusters is used to select the best patterns. A pattern is considered significant when this ratio is statistically better than the ratios obtained for clouds of points following a Gaussian distribution. The patterns inferred for the left cingulate sulcus are consistent with the patterns described in the atlas of Ono.