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- Title
Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier.
- Authors
Mao, Keming; Deng, Zhuofu
- Abstract
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.
- Subjects
PULMONARY nodules; CHEST X rays; COMPUTED tomography; IMAGE analysis; COMPARATIVE studies
- Publication
Computational & Mathematical Methods in Medicine, 2016, p1
- ISSN
1748-670X
- Publication type
Article
- DOI
10.1155/2016/1091279