基于NSCT和SVM的脑MRI医学图像识别

Brain MRI image recognition based on nonsubsampled contourlet transform and SVM

利用NSCT变换具有多尺度和平移不变性,能够稀疏地表示纹理图像的特点,将具有丰富纹理信息的人体脑部核磁共振(MR)图像,从空间域变换到频率域表示。提取变换后表征图像特性的低频子带均值、方差及高频16个方向子带能量作为特征向量,输入SVM分类器进行分类识别。实验结果表明该方法对非病变脑部MR图像识别准确率达到100%,病变脑部MR图像的识别率达到90.90%,综合识别率达到95.45%。且该方法提取的特征维数少,识别速度快,识别率高,能够快速区分病变与非病变脑部MR图像。

Using the characteristics of multi-scale selection and shift invariance of nonsubsampled contourlet transform （NSCT）,the features of texture images can be depicted sparsely,and the magnetic resonance（MR）brain images with plenty of texture information can be converted from spatial domain to frequency domain. The mean value and variance of low-frequency subband,and energy of high frequency sixteen direction subbands that can feature the image characters are extracted as feature vectors,and sent into the classifier of support vector machine for classification and identification. The experimental results show that the method＆#39;s recognition accuracy rate of normal and abnormal brain MR images is 100% and 90.90% respectively,and the mean recognition rate is up to 95.45%. This method can distinguish the normal and abnormal brain MR images quickly with less feature dimensions.