標題: Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree
作者: Chao, Wen-Hung
Chen, You-Yin
Cho, Chien-Wen
Lin, Sheng-Huang
Shih, Yen-Yu I.
Tsang, Siny
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: MRI;Image segmentation;Boosted decision tree;Brain tissue classification;Accuracy rate;k index
公開日期: 15-Nov-2008
摘要: 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 1, Vetter M, Wegner 1, Bottger T, Nolden M, Schobinger M, et al. The medical imaging interaction toolkit. Med Imag Anal 2005;9:594-604] and an adaptive segmentation (AS) algorithm [Wells WM, Crimson 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. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.jneumeth.2008.08.017
http://hdl.handle.net/11536/8142
ISSN: 0165-0270
DOI: 10.1016/j.jneumeth.2008.08.017
期刊: JOURNAL OF NEUROSCIENCE METHODS
Volume: 175
Issue: 2
起始頁: 206
結束頁: 217
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