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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 572494, 16 pages
Research Article

Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

1SCAD Institute of Technology, Palladam, Coimbatore 641664, India
2Department of ECE, RVS College of Engineering and Technology, Dindigul 624005, India

Received 11 August 2014; Revised 23 October 2014; Accepted 23 October 2014; Published 26 November 2014

Academic Editor: Issam El Naqa

Copyright © 2014 Ravichandran C. Gopalakrishnan and Veerakumar Kuppusamy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.