An adaptive lung nodule detection algorithm

Authors:
Wei Guo;Ying Wei;Hanxun Zhou;DingYe Xue
Affiliations:
School of Information Science & Engineering, Northeastern University;School of Information Science & Engineering, Northeastern University;School of Information Science & Engineering, Northeastern University;School of Information Science & Engineering, Northeastern University
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 4
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Digital Image Processing Using MATLAB

Digital Image Processing Using MATLAB
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)

Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Lung nodule detection in low-dose and thin-slice computed tomography

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.