基于MRI的肝硬化程度的计算机辅助诊断  

Computer Aided Diagnosis on Hepatocirrhosis Degree Based on MRI

作　　者：姜慧研[1] 赵越[2] 杨新风[1] 

机构地区：[1]东北大学计算中心,辽宁沈阳110004 [2]东北大学中荷生物医学与信息工程学院,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2007年第1期31-34,共4页Journal of Northeastern University(Natural Science)

基　　金：辽宁省自然科学基金资助项目(20042020)

摘　　要：提出了将肝脏纹理特征与形状特征相结合以实现计算机辅助诊断肝硬化程度的新方法.首先利用图像分割技术从腹部MRI中分割肝脏区域,并提取肝脏区域的纹理特征与形状特征;然后用共轭梯度算法改进BP神经网络,基于该网络与纹理特征计算肝脏纤维化的程度,并利用形状特征计算肝左叶钝化的程度;最后结合肝脏纤维化程度与肝左叶钝化程度进行肝硬化程度的诊断.将该方法应用于实际的腹部MRI图像,证明了其对肝硬化程度诊断的准确率显著高于单一神经网络的方法.A new computer aided diagnosis （CAD） method for hepatocirrhosis is proposed, based on the recognition of characteristics of liver texture and shape. In the method, the liver region is segmented from the abdominal MRI-based image through image segmentation technique, and the characteristics of liver texture and shape are extracted from liver region. Then, the degree of liver fibrosis is calculated according to its texture and the improved BP neural network by conjugate gradient algorithm, and the passivation of left liver leaf is calculated according to its shape characteristics. Finally, the hepatocirrhosis is diagnosed taking the degrees of liver fibrosis and passivation as criteria. The method has been applied to the practical abdominal MRI, and showed higher accuracy in the diagnosis of hepatocirrhosis than single neural network.

关 键 词：肝硬化 MRI 图像分割 BP神经网络 模式识别 计算机辅助诊断 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]