Brain tissue segmentation in 4D CT using voxel classification
R. van den Boom, Marcel T. H. Oei, S. Lafèbre, Luuk J. Oostveen, Frederick J. A. Meijer, Stefan C. A. Steens, Mathias Prokop, Bram van Ginneken, Rashindra Manniesing · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.