摘要
针对脑白质疏松症MR图像白质区域静脉信息复杂的特点,提出一种基于小波变换的多阈值脑白质疏松症MR图像的静脉提取方法.首先,利用形态学变换对图像中的细节信息进行增强处理;其次,利用小波变换的多分辨率特性对图像的灰度直方图进行多层小波分解,对不同层次的小波细节信息及逼近信息进行阈值处理并重构,提取出静脉的灰度特征;最后,利用多阈值分割方法将脑白质区域的静脉分割出来.实验结果表明,该方法能实现静脉信息的自动快速提取,并实现了静脉的量化分析,为医生对脑白质疏松症患者白质区域静脉扩张程度的诊断提供一个定量标准,具有临床辅助诊断价值.
The venous＇s information show in Leukoaraiosis＇s MR image is complex, on account of this characteristic, a multi-threshold venous extraction method was proposed in this paper. First, the venous detail information was enhanced by using morphological top-hat transform; Second, the image＇s histogram was processed by using multilayer wavelet decomposition, then threshold processing and reconstruction were used at different levels by wavelet detail information and approximation information to extract venous gray feature; Finally, the multi-threshold segmentation method was used to obtain venous regions. The results show that this method can extract venous information automatically and quickly, furthermore, it realized the venous＇s quantitative analysis. It provided a quantitative criterion for doctors＇ diagnosis of venous expansion in white matter regions of Leukoaraiosis and had clinical assistant diagnosis value.
出处
《计算机系统应用》
2012年第11期84-88,144,共6页
Computer Systems & Applications
基金
国家自然科学基金项目(30770685)
关键词
脑白质疏松症
变换
多阈值分割
小波变换
静脉分割
Leukoaraiosis
top-hat transform
multi-threshold segmentation
wavelet transformation
venous extraction
