计算机辅助诊断技术在脑部CT诊断中的应用

Application of computer-aided diagnosis technique in the diagnosis of brain CT

近年来计算机辅助诊断成为医学影像学中的研究热点课题之一,将基于灰度共生矩阵和BP神经网络的计算机辅助诊断技术应用到脑CT图像诊断中。首先,在分析灰度共生矩阵的基础上提取了能量、对比度、相关度和熵等纹理特征参数;然后,利用BP神经网络设计了一个分类器,用来对特征向量进行分类;最后,实验结果表明此方法可以有效的区分正常与异常的脑部CT图像,为医师的诊断提供辅助信息。

In recent years, computer-aided diagnosis has become one of the most popular topic in the field of medical imaging. Computer aided-diagnosis technology based on GLCM and BP Neural Network is applied to the diagnosis of brain CT images in this paper. Firstly, texture features such as energy, contrast, relevance and entropy are extracted on the basis of analysis of GLCM. Secondly, a classifier designed by using BP neural network is used to classify the feature vectors. Finally, the experimental results show that this method can effectively distinguish between normal and abnormal brain CT images, it provides auxiliary information for the doctors＇ diagnosis.