Automatic inference of sulcus patterns using 3D moment invariants

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.