Document Detail


Classification of lung data by sampling and support vector machine.
MedLine Citation:
PMID:  17270959     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
Developing a Computer-Assisted Detection (CAD) system for automatic detection of pulmonary nodules in thoracic CT is a highly challenging research area in the medical domain. It requires the application of state-of-the-art image processing and pattern recognition technologies. The object recognition and feature extraction phase of such a system generates a large number of data set. As there is normally a large quantity of non-nodule objects within this data set while the nodule objects are sparse, a Gaussian mixture model-based sampling method is used to reduce the non-nodule data and thus the classification complexity. The support vector machine, a classifier motivated from the statistical learning theory, is used in the pattern recognition stage of automatic pulmonary nodule detection. After the training process, only support vectors will be used in the classification process. As the support vector machine classifier gives the unique optimal solution, the experiment on the lung nodule data shows a fast and satisfactory classification rate.
Authors:
Jamshid Dehmeshki; Jun Chen; Manlio Valdivieso Casique; Mustafa Karakoy
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  5     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2004  
Date Detail:
Created Date:  2007-02-02     Completed Date:  2007-06-08     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  3194-7     Citation Subset:  -    
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