Computerized Medical Imaging and Graphics 33 (2009) 111–121 Contents lists available at ScienceDirect Computerized Medical Imaging and Graphics journal homepage: www.elsevier.com/locate/compmedimag Automatic segmentation of magnetic resonance images using a decision tree with spatial information Wen-Hung Chao a,b , You-Yin Chen a,∗ , Sheng-Huang Lin c , Yen-Yu I. Shih d , Siny Tsang e a Department of Electrical and Control Engineering, National Chiao Tung University, No. 1001, Ta-Hsueh Rd., Hsinchu 300, Taiwan, ROC b Department of Biomedical Engineering, Yuanpei University, No. 306, Yuanpei St., Hsinchu 300, Taiwan, ROC c Department of Neurology, Buddhist Tzu Chi General Hospital, No. 707, Sec. 3, Chung Yang Rd., Hualien 970, Taiwan, ROC d Institute of Biomedical Sciences, Academia Sinica, No. 128, Sec. 2, Academia Rd., Taipei 115, Taiwan, ROC e College of Criminal Justice, Sam Houston State University, Huntsville, TX 77341-2296, USA article info Article history: Received 2 December 2007 Received in revised form 21 October 2008 Accepted 30 October 2008 Keywords: Automatic segmentation Decision tree Spatial information Wavelet transform Accuracy rates abstract Here we proposed an automatic segmentation method based on a decision tree to classify the brain tissues in magnetic resonance (MR) images. Two types of data – phantom MR images obtained from IBSR (http://www.cma.mgh.harvard.edu/ibsr) and simulated brain MR images obtained from BrainWeb (http://www.bic.mni.mcgill.ca/brainweb) – were segmented using an automatic decision tree algorithm to obtain images with improved visual rendition. Spatial information on the general gray level (G), spatial gray level (S), and two-dimensional wavelet transform (W) was combined in-plane in two coordinate systems (Euclidean coordinates (x, y) or polar coordinates (r, )). The decision tree was constructed based on a binary tree with nodes created by splitting the distribution of input features of the tree. The spatial information obtained from MR images with different noise levels and inhomogeneities were segmented to compare whether the use of a decision tree improved the identification of human anatomical structures in a neuroimage. The average accuracy rates of segmentation for phantom images with a noise variation of 15 gray levels were 0.9999 and 0.9973 with spatial information (G, x, y, r, ) and (S, x, y, r, ), respectively, and 0.9999 and 0.9819 with spatial information (G, x, y, S, r, ) and (W, x, y, G, r, ). The average accuracy rates of segmentation for simulated MR images with a noise level of 5% were 0.9532 and 0.9439 with spatial information (G, x, y, r, ) and (S, x, y, r, ), respectively, and 0.9446 and 0.9287 with spatial information (G, x, y, S, r, ) and (W, x, y, G, r, ). The accuracy rates of segmentation were highest for both simulated phantom and brain MR images, having the lowest noise levels, from a reduction of overlapping gray levels in the images. The accuracies of segmentation were higher when the spatial information included the general gray level than when it included the spatial gray level, which in turn were higher than when it included the wavelet transform. Furthermore, the performance of segmentation was also evaluated with a boundary detection methodology that is based on the Hausdorff distance to compare with the mean computer to observer difference (COD) and mean interobserver difference (IOD) for gray matter (GM), white matter (WM), and all areas (ALL) from images segmented using the decision tree. The values of mean COD are similar and around 12mm for GM segmented using the decision tree. Our segmentation method based on a decision tree algorithm presented an easy way to perform automatic segmentation for both phantom and tissue regions in brain MR images. © 2008 Published by Elsevier Ltd. 1. Introduction Magnetic resonance (MR) imaging is widely used in clinical diagnosis. Segmentation is one of the techniques used to classify the brain tissues in MR images, which is a basic problem for identifying anatomical structures in MR image processing. Several segmen- ∗ Corresponding author. Tel.: +886 3 571 2121x54427; fax: +886 3 612 5059. E-mail address: irradiance@so-net.net.tw (Y.-Y. Chen). tation methods have been applied in the analysis of anatomical structures involving three-dimensional (3D) reconstruction, tissue- type contour definition, clinical diagnosis [1,2], and in cortical surface segmentation, volume assessment of brain tissue, tissue classification, tumor segmentation, and characterization of vari- ous brain diseases such as sclerosis, epilepsy, stroke, cancer, and Alzheimer’s disease [3,4]. The accuracy of segmenting the cortical surface for analyzing the volumes of different tissues, such as gray matter (GM) and white matter (WM), significantly affects clinical diagnoses. It this is made difficult by the presence of imaging noise 0895-6111/$ – see front matter © 2008 Published by Elsevier Ltd. doi:10.1016/j.compmedimag.2008.10.008