An Adaptive Lung Nodule Detection Algorithm

摘要：

An adaptive lung nodule detection algorithm is presented in computed tomography (CT) images. Here, we present the details of the proposed algorithm and provide a performance analysis using a database from the department of radiology. Our algorithm consists of a feature selected part and a feature classified part. In the feature selected part, eight image features are extracted and Support Vector Machine (SVM) approach is applied to evaluate the classified performance of each feature. In the feature classified part, a nonlinear classifier is constructed on the basis of modified Mahalanobis distance. The adaptive algorithm is used to adjust the threshold in the classifier. The experiment indicated that the algorithm has a good sensitivity and accuracy for lung nodule detection.

关键词： lung nodule detection feature eztraction the Support Vector Machine modified Mahalanobis distance vector an adaptive classification

作者: Wei Guo Ying Wei Hanxun Zhou DingYe Xue

作者单位: School of Information Science & Engineering, Northeastern University

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2361-2365

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）