摘要
为改善病灶形状不规则、纹理结构简单等因素对计算机辅助肺CT中病灶检测精度的影响,提出将疑似病灶与整体肺区的相对位置关系作为传统形状、纹理特征之外的一种新的隐变量,参与训练过程.为符合病灶的三维特征,引入基于三维矩阵模式的SVM,进一步设计含隐变量三维矩阵模式SVM.将吉林省肿瘤医院的150例病例建立数据库,用其余三种SVM方案与本文方案进行比较,文中算法可达到97.05%的真阳性和9.21%的假阳性,证明其优越性及辅助放疗师的有效性.
Accuracy of Computer Aided Detection （ CAD ） of lung lesions in chest CT may be affected by irregular shapes and simple texture of the lesions. To improve the poor performance of current CAD schemes, relative position from the suspected lesion to the whole lung area is added as a latent variable on the basis of traditional texture and shape features, which also participates in optimizing the SVM. Furthermore, considering 3D feature of the lung lesions, 3D matrixes based SVM （3D SVM） is combined into the Latent SVM （L-SVM） to design 3D SVM with latent variables （3 D-L-SVM）. 150-case database from Jilin Tumor Hospital is used to validate the proposed algorithm. The performances of other three CAD schemes are compared on the same database. True the false positives of 9.21%. The experimental results and effectiveness of assisting the radiologists. positives of the 3 D-L-SVM achieves 97.5 % with verify the advantages of the proposed algorithm
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2013年第5期460-466,共7页
Pattern Recognition and Artificial Intelligence
基金
吉林省科技发展计划资助项目(No.201201107)
