摘要
利用单幅CT图像进行肺部节结的识别存在较大的局限性,故把多幅相邻图像组成的短图像序列引入自动识别的过程,并根据节结的球形结构,把节结感兴趣区域(ROI)对应的原始图像看做是二维函数的三维表面,提取不同于传统图像区域特征的刻画三维表面形状且反映节结在短图像序列中变化情况的新型特征。最后用支持向量机(SVM)进行分类实验,验证了所提取特征的有效性。
Concerning the limitation of using single CT image to detect the lung nodule, short image series consisting of a few sequential CT image was used in nodule＇s auto-detection in this paper. Meanwhile, the image corresponding to the Region of Interest （ROI） was taken as a surface of some 2-d function. Then the new features, different from traditional image region features, were extracted, which depicted the surface＇s shape and its variances in short image series. At last, the effectiveness of the extracted features is proved by using the Support Vector Machine （SVM）.
出处
《计算机应用》
CSCD
北大核心
2010年第11期2988-2990,2994,共4页
journal of Computer Applications
关键词
肺部节结
特征提取
计算机辅助诊断
图像序列
胸部CT图像
支持向量机
pulmonary nodule
feature extraction
Computer-Aided Diagnosis （CAD）
image series
chest CT image
Support Vector Machine （SVM）
