Defining cortical sulcus patterns using partial clustering based on bootstrap and bagging

The cortical folding patterns are very different from one individual to another. Here we try to find folding patterns automatically using large-scale datasets by non-supervised clustering analysis. The sulci of each brain are detected and identified using the brain VIS A open software. The 3D moment invariants are calculated and used as the shape descriptors of the sulci identified. A partial clustering algorithm using bootstrap sampling and bagging (PCBB) is devised for cortical pattern mining. Partial clusters are found using a modified hierarchical clustering method constrained by an objective function which looks for the most compact and dissimilar clusters. Bagging is used to increase stability. Experiments on simulated and real datasets are used to demonstrate the strength and stability of this algorithm compared to other standard approaches. Some cortical patterns are found using our method. In particular, the patterns found for the left cingulate sulcus are consistent with the patterns described in the atlas of Ono.