Cao Lei, Li Wei-juan, Feng Qian-jin
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2011 Feb;31(2):324-8.
Based on suspected pulmonary nodule segmentation images obtained previously and with a large-sample training, automatic detection and diagnosis of the pulmonary nodules on CT images was realized by extracting the multi-dimensional features of the pulmonary nodule images and the application of LDA and SVM statistical classifiers. Experimental results showed that this detection and diagnosis method produced better classification results, and is practical for application in CAD systems.
基于先前获得的疑似肺结节分割图像并经过大样本训练,通过提取肺结节图像的多维特征以及应用线性判别分析(LDA)和支持向量机(SVM)统计分类器,实现了CT图像上肺结节的自动检测与诊断。实验结果表明,这种检测与诊断方法产生了较好的分类结果,并且在计算机辅助诊断(CAD)系统中具有实际应用价值。