Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree

Abstract

The purpose of this study was to improve the accuracy rate of brain tissue classification in magnetic resonance (MR) imaging using a boosted decision tree segmentation algorithm. Herein, we examined simulated phantom MR (SPMR) images, simulated brain MR (SBMR) images, and a real data. The accuracy rate and k index when classifying brain tissues as gray matter (GM), white matter (WM), or cerebral-spinal fluid (CSF) were better when using the boosted decision tree algorithm combined with a fuzzy threshold than when using a statistical region-growing (SRG) algorithm [Wolf I, Vetter M, Wegner I, Böttger T, Nolden M, Schöbinger M, et al. The medical imaging interaction toolkit. Med Imag Anal 2005;9:594–604] and an adaptive segmentation (AS) algorithm [Wells WM, Grimson WEL, Kikinis R, Jolesz FA. Adaptive segmentation of MRI data. IEEE Trans Med Imag 1996;15:429–42]. The segmentation performance when using this algorithm on real data from brain MR images was also better than those of SRG and AS algorithm. Segmentation of a real data using the boosted decision tree produced particularly clear brain MR imaging and permitted more accurate brain tissue segmentation. In conclusion, a decision tree with appropriate boost trials successfully improved the accuracy rate of MR brain tissue segmentation.