现代电子技术
現代電子技術
현대전자기술
MODERN ELECTRONICS TECHNIQUE
2014年
12期
63-66,69
,共5页
NSCT%脑部MR图像%纹理特征%支持向量机
NSCT%腦部MR圖像%紋理特徵%支持嚮量機
NSCT%뇌부MR도상%문리특정%지지향량궤
NSCT%MR brain images%texture feature%support vector machine
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。
利用NSCT變換具有多呎度和平移不變性，能夠稀疏地錶示紋理圖像的特點，將具有豐富紋理信息的人體腦部覈磁共振（MR）圖像，從空間域變換到頻率域錶示。提取變換後錶徵圖像特性的低頻子帶均值、方差及高頻16箇方嚮子帶能量作為特徵嚮量，輸入SVM分類器進行分類識彆。實驗結果錶明該方法對非病變腦部MR圖像識彆準確率達到100%，病變腦部MR圖像的識彆率達到90.90%，綜閤識彆率達到95.45%。且該方法提取的特徵維數少，識彆速度快，識彆率高，能夠快速區分病變與非病變腦部MR圖像。
이용NSCT변환구유다척도화평이불변성，능구희소지표시문리도상적특점，장구유봉부문리신식적인체뇌부핵자공진（MR）도상，종공간역변환도빈솔역표시。제취변환후표정도상특성적저빈자대균치、방차급고빈16개방향자대능량작위특정향량，수입SVM분류기진행분류식별。실험결과표명해방법대비병변뇌부MR도상식별준학솔체도100%，병변뇌부MR도상적식별솔체도90.90%，종합식별솔체도95.45%。차해방법제취적특정유수소，식별속도쾌，식별솔고，능구쾌속구분병변여비병변뇌부MR도상。
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'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.
