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Proceedings Paper

Classification of pulmonary nodules in lung CT images using shape and texture features
Author(s): Ashis Kumar Dhara; Sudipta Mukhopadhyay; Anirvan Dutta; Mandeep Garg; Niranjan Khandelwal; Prafulla Kumar
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Paper Abstract

Differentiation of malignant and benign pulmonary nodules is important for prognosis of lung cancer. In this paper, benign and malignant nodules are classified using support vector machine. Several shape-based and texture-based features are used to represent the pulmonary nodules in the feature space. A semi-automated technique is used for nodule segmentation. Relevant features are selected for efficient representation of nodules in the feature space. The proposed scheme and the competing technique are evaluated on a data set of 542 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The nodules with composite rank of malignancy “1",”2" are considered as benign and “4",”5" are considered as malignant. Area under the receiver operating characteristics curve is 0:9465 for the proposed method. The proposed method outperforms the competing technique.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852Y (24 March 2016); doi: 10.1117/12.2214466
Show Author Affiliations
Ashis Kumar Dhara, Indian Institute of Technology Kharagpur (India)
Sudipta Mukhopadhyay, Indian Institute of Technology Kharagpur (India)
Anirvan Dutta, Birla Institute of Technology Mesra (India)
Mandeep Garg, Postgraduate Institute of Medical Education & Research (India)
Niranjan Khandelwal, Postgraduate Institute of Medical Education & Research (India)
Prafulla Kumar, Dept. of Electronics and Information Technology (India)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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