多维分形维数分析多发性硬化患者表现正常脑白质  

Multiple Fractal Analysis on the Research of Normal Appearing White Matter in Patients with Multiple Sclerosis

作　　者：聂书君[1] 童忠勇[1] 于春水[2] 童隆正[1] 

机构地区：[1]首都医科大学生物医学工程学院计算机系 [2]首都医科大学宣武医院放射科

出　　处：《首都医科大学学报》2007年第3期359-362,共4页Journal of Capital Medical University

基　　金：国家自然科学基金(30670575);北京市自然科学基金(3073015)资助项目~~

摘　　要：目的测试多发性硬化(MS)患者表现正常的脑白质(NAWM)与健康志愿者的正常脑白质(NWM)的纹理差异是否有统计学意义,并建立判别模型对两类脑白质(WM)进行分类。方法用估计分形维数的方法分析多发性硬化患者和健康志愿者MR的T2加权图像的感兴趣区(RO I),得到二维分形维数、三维表面分形维数和三维体积分形维数,依据这些特征参量用概率神经网络(PNN)对样本分类,然后与基于灰度共生矩阵和游程长纹理分析方法建立的模型进行对比。结果MS表现正常组和正常组WM的3个分形维数差异有统计学意义(P<0.05),MS表现正常组与正常组的识别率分别为77.5%和65%。结论在本批样本中,NAWM和正常WM的纹理差异有统计学意义,以上结论还需扩大样本量并采用多种方法进一步证实。Objective To discriminate the differences between normal appearing white matter （NAWM） in the patients with multiple sclerosis（MS） and normal white matter （NWM） in the healthy, and to set up a model to discriminate two classes. Methods The total sample set was 120 images with NAWM and NWM of 60 each. The NAWM sample set of 60 was randomly divided into training section and testing section by rate of 70% and 30% and the NWM was processed with the same way. Multiple fractal analysis was used to analyze regions of interest（ROI） which were gained from T2-weighted MR images of patients with MS and the healthy. Three eigenvectors were obtained, which were fractal box dimensions for 2D, 3D surface and 3D volume. The sample was conversed to binary image firstly and different patterns of square were used to cover it. A data counting the covering squares was obtained from one pattern and a data set was achieved from all patterns. After logarithms of the square sizes and the data set gained were extracted, the linear fitting was conducted to calculate slope by these logarithms, i.e. fractal box dimension for 2D. Similarly to the fractal box dimension for 2D, a curved face was constructed based on the grey value of every pixel firstly, and then a 3D surface was achieved following by covering the face with different sizes of cube. After logarithms were applied to the cube sizes and the data set gained, linear fitting slope was acquired, i.e. fractal box dimension for 3D surface. When the whole volume was covered by different sizes of cube, fractal box dimension for 3D volume was then achieved. T-test was conducted to test if there were any significant differences in three eigenvectors of fractal box dimensions above between two groups of NAWM and NWM. The probabilistic neural network （ PNN ） was used to classify ROI based on significant eigenvectors. Results In two groups of NAWM and NWM, the mean values were 1. 803 vs 1. 833 for fractal box dimension for 2D （P = 0.037）, 1.9751 vs 2.058 for fractal box

关 键 词：多维分形维数 多发性硬化 表现正常脑白质 

分 类 号：R445.2[医药卫生—影像医学与核医学]