Review of fast density-peaks clustering and its application to pediatric white matter tracts

Abstract

Clustering white matter (WM) tracts from diffusion tensor imaging (DTI) is primarily important for quantitative analysis on pediatric brain development. A recently developed algorithm, density peaks (DP) clustering, demonstrates great robustness to the complex structural variations of WM tracts without any prior templates. Nevertheless, the calculation of densities, the core step of DP, is time consuming especially when the number of WM fibers is huge. In this paper, we propose a fast algorithm that accelerates the density computation about 50 times over the original one. We convert the global calculation for the density as well as critical parameter in the process into local computations, and develop a binary tree structure to orderly store the neighbors for these local computations. Hence, the density computation turns out to direct access of the structure, rendering significantly computational saving. Experiments on synthetic point data and the JHU-DTI data set validate the efficiency and effectiveness our fast DP algorithm compared with existing clustering methods. Finally, we demonstrate the application of the proposed algorithm on the analysis of pediatric WM tract development.