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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 1091279, 7 pages
Research Article

Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier

College of Software, Northeastern University, Shenyang, Liaoning Province 110004, China

Received 5 September 2016; Accepted 6 November 2016

Academic Editor: Ayman El-Baz

Copyright © 2016 Keming Mao and Zhuofu Deng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP) is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.