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Proceedings Paper

An automated lung nodule detection system for CT images using synthetic minority oversampling
Author(s): Shrikant A. Mehre; Sudipta Mukhopadhyay; Anirvan Dutta; Nagam Chaithan Harsha; Ashis Kumar Dhara; Niranjan Khandelwal
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Paper Abstract

Pulmonary nodules are a potential manifestation of lung cancer, and their early detection can remarkably enhance the survival rate of patients. This paper presents an automated pulmonary nodule detection algorithm for lung CT images. The algorithm utilizes a two-stage approach comprising nodule candidate detection followed by reduction of false positives. The nodule candidate detection involves thresholding, followed by morphological opening. The geometrical features at this stage are selected from properties of nodule size and compactness, and lead to reduced number of false positives. An SVM classifier is used with a radial basis function kernel. The data imbalance, due to uneven distribution of nodules and non-nodules as a result of the candidate detection stage, is proposed to be addressed by oversampling of minority class using Synthetic Minority Over-sampling Technique (SMOTE), and over-imposition of its misclassification penalty. Experiments were performed on 97 CT scans of a publically-available (LIDC-IDRI) database. Performance is evaluated in terms of sensitivity and false positives per scan (FP/scan). Results indicate noteworthy performance of the proposed approach (nodule detection sensitivity after 4-fold cross-validation is 92.91% with 3 FP/scan). Comparative analysis also reflects a comparable and often better performance of the proposed setup over some of the existing techniques.

Paper Details

Date Published: 24 March 2016
PDF: 8 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850H (24 March 2016);
Show Author Affiliations
Shrikant A. Mehre, Indian Institute of Technology Kharagpur (India)
Sudipta Mukhopadhyay, Indian Institute of Technology Kharagpur (India)
Anirvan Dutta, Birla Institute of Technology Mesra (India)
Nagam Chaithan Harsha, National Institute of Technology, Durgapur (India)
Ashis Kumar Dhara, Indian Institute of Technology Kharagpur (India)
Niranjan Khandelwal, Postgraduate Institute of Medical Education & Research (India)

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

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