Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70162
Title: A strategy for SPN detection based on biomimetic pattern recognition and knowledge-based features
Authors: Liang, Y
He, ZS
Liu, Y
Keywords: Feature extraction
Solitary pulmonary nodules detection
Biomimetic pattern recognition
Issue Date: 2009
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2009, v. 5579, p. 672-681 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Image processing techniques have proved to be effective in improving the diagnosis of lung nodules. In this paper, we present a strategy for solitary pulmonary nodules (SPN) detection using radiology knowledge-based feature extraction scheme and biomimetic pattern recognition (BPR). The proposed feature extraction scheme intends to synthesize comprehensive information of SPN according to radiology knowledge, e.g. grey level features, morphological, texture and spatial context features. Using support vector machine (SVM), Naive Bayes (NB) and BPR as the classifiers to evaluate different feature representation schemes, our experimental study shows that the proposed radiology knowledge-based features can significantly improve the classification effectiveness of SPN detection from nonnodules, in terms of accuracy and F1 value, regardless of the classifiers used. We also note that BPR can deliver a consistent performance using our knowledge-based features, even the ratios between nonnodules and nodules are quite different in the training set.
Description: The 22nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2009), Tainan, Taiwan ROC, 24-27 June 2009
URI: http://hdl.handle.net/10397/70162
ISBN: 978-3-642-02567-9
978-3-642-02568-6
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-02568-6_68
Appears in Collections:Conference Paper

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