Automatic segmentation of magnetic resonance images using a decision tree with spatial information
Automatic segmentation of magnetic resonance images using a decision tree with spatial information
复制标题
DOI：
10.1016/j.compmedimag.2008.10.008
复制
发表时间：
2009-03-01
影响因子：
5.7
通讯作者：
Tsang, Siny
中科院分区：
文献类型：
--
作者：
Chao, Wen-Hung;Chen, You-Yin;Tsang, Siny
本文提出了一种基于决策树的自动分割方法来对磁共振图像中的脑组织进行分类。使用自动决策树算法对两种类型的数据-从IBSR(http://www.cma.mgh.harvard.edu/ibsr))获得的体模MR图像和从BrainWeb(http://www.bic.mni.mcgill.ca/brainweb))获得的模拟脑MR图像-进行分割，以获得具有改进的视觉再现的图像。关于一般灰度级(G)、空间灰度级(S)和二维小波变换(M是在两个坐标系(欧几里得坐标系(x，y)或极坐标(r，theta))中平面内组合的空间信息)。决策树是基于二叉树构建的，二叉树的节点是通过对树的输入特征的分布进行拆分而创建的。从具有不同噪声水平和不均匀的MR图像中获得的空间信息被分割，以比较决策树的使用是否改善了神经图像中人体解剖结构的识别。对于噪声变化为15个灰度的体模图像，空间信息(G，x，y，r，theta)和(S，x，y，r，theta)的平均分割准确率分别为0.9999和0.9973，空间信息(G，x，y，S，r，theta)和(W，x，y，G，r，theta)的平均分割准确率分别为0.9999和0.9819。对于噪声水平为5%的模拟MR图像，空间信息(G，x，y，r，theta)和(S，x，y，r，theta)的平均分割准确率分别为0.9532和0.9439，空间信息(G，x，y，S，r，theta)和(W，x，y，G，r，theta)的平均分割准确率分别为0.9446和0.9287。由于减少了图像中重叠的灰度级，对于具有最低噪声水平的模拟体模和脑MR图像，分割的准确率最高。当空间信息包含总灰度级时，分割精度高于包含空间灰度级时，后者又高于包含小波变换时的分割精度。此外，还使用基于Hausdorff距离的边界检测方法评估了分割的性能，并将其与使用决策树分割的图像的灰质(GM)、白质(WM)和所有区域(ALL)的平均计算机到观察者之间的差(COD)和平均观察者间差(IOD)进行了比较。用决策树分割出的GM，其平均COD值相似，在12 mm左右。我们的基于决策树算法的分割方法提供了一种简单的方法来对脑MR图像中的模型和组织区域进行自动分割。(C)2008年，爱思唯尔有限公司出版。
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 (M was combined in-plane in two coordinate systems (Euclidean coordinates (x,y) or polar coordinates (r,theta)). 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, theta) and (S, x, y, r, theta), respectively, and 0.9999 and 0.9819 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). 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, theta) and (S, x, y, r, theta), respectively, and 0.9446 and 0.9287 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). 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 12 mm 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. (C) 2008 Published by Elsevier Ltd.