Comparison of Multispectral Image-Processing Methods for Brain Tissue Classification in BrainWeb Synthetic Data and Real MR Images.


Chen HM(#)(1)(2)(3), Chen HC(#)(4)(5), Chen CC(4), Chang YC(4)(6), Wu YY(4)(6), Chen WH(4), Sung CC(1), Chai JW(4)(7), Lee SK(4)(8).
Author information:
(1)Center for QUantitative Imaging in Medicine
(CQUIM), Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
(2)Department of Biomedical Engineering, Hungkuang University, Taichung, Taiwan.
(3)Department of Computer Science & Information Engineering, National United University, Miaoli, Taiwan.
(4)Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan.
(5)School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan.
(6)Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan.
(7)Section of Radiology, College of Medicine, China Medical University, Taichung, Taiwan.
(8)Chief Strategy Officer, Tungs' Taichung MetroHarbor Hospital, Taichung, Taiwan.
(#)Contributed equally


Accurate quantification of brain tissue is a fundamental and challenging task in neuroimaging. Over the past two decades, statistical parametric mapping (SPM) and FMRIB's Automated Segmentation Tool (FAST) have been widely used to estimate gray matter (GM) and white matter (WM) volumes. However, they cannot reliably estimate cerebrospinal fluid (CSF) volumes. To address this problem, we developed the TRIO algorithm (TRIOA), a new magnetic resonance (MR) multispectral classification method. SPM8, SPM12, FAST, and the TRIOA were evaluated using the BrainWeb database and real magnetic resonance imaging (MRI) data. In this paper, the MR brain images of 140 healthy volunteers (51.5 ± 15.8 y/o) were obtained using a whole-body 1.5 T MRI system (Aera, Siemens, Erlangen, Germany). Before classification, several preprocessing steps were performed, including skull stripping and motion and inhomogeneity correction. After extensive experimentation, the TRIOA was shown to be more effective than SPM and FAST. For real data, all test methods revealed that the participants aged 20-83 years exhibited an age-associated decline in GM and WM volume fractions. However, for CSF volume estimation, SPM8-s and SPM12-m both produced different results, which were also different compared with those obtained by FAST and the TRIOA. Furthermore, the TRIOA performed consistently better than both SPM and FAST for GM, WM, and CSF volume estimation. Compared with SPM and FAST, the proposed TRIOA showed more advantages by providing more accurate MR brain tissue classification and volume measurements, specifically in CSF volume estimation.