Unsupervised identification of white matter tracts in a mouse brain using a directional correlation-based region growing (DCRG) algorithm

The inner product of the major eigenvectors of adjacent pixels, known as the directional correlation (DC), has been used previously as a quantitative index to investigate directional similarity in white matter (WM) tracts. A high degree of directional similarity (i.e., high DC) among pixels within individual WM tracts was observed. Based on this observation, a region growing algorithm was employed to propagate an area from a seed point as a function of the DC threshold (DCt) to manually identify WM tracts in two-dimensional (2D) slices from diffusion tensor imaging (DTI). In the present study, an improved unsupervised DC based region growing (DCRG) method was implemented to reduce the operator-dependent variance and to improve the ease of use of the technique. By employing improved method, a multi-slice DTI data set of an in vivo mouse brain was used to identify the external capsule, visual pathway, and corpus callosum. The resultant WM tracts are computer rendered in three-dimensional (3D) images with anatomical images as structural references. In addition, three sets of ex vivo mouse brain data were used to examine the effects of different slice thickness and the signal-to-noise ratio (SNR) to the outcome of DCRG.