Segmentation of brain from computed tomography head images.

An algorithm to determine the human brain (gray matter (GM) and white matter (WM)) from computed tomography (CT) head volumes with large slice thickness is proposed based on thresholding and brain mask propagation. Firstly, a 2D reference image is chosen to represent the intensity characteristics of the original 3D data set. Secondly, the region of interest of the reference image is determined as the space enclosed by the skull. Fuzzy C-means clustering is employed to determine the threshold for head mask and the low threshold for brain segmentation. The high threshold is calculated as the weighted intensity average of the boundary pixels between bones and GM/WM. Based on the low and high thresholds, the CT volume is binarized, followed by finding the brain candidates through distance criterion. Finally the brain is identified through brain mask propagation using the spatial relationship of neighboring axial slices. The algorithm has been validated against one non-enhanced CT and one enhanced CT volume with pathology.