Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-07
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Mao, Keming& Deng, Zhuofu. 2016. Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1100057
Modern Language Association (MLA)
Mao, Keming& Deng, Zhuofu. Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1100057
American Medical Association (AMA)
Mao, Keming& Deng, Zhuofu. Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1100057
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100057