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Ant colony optimization approaches to clustering of lung nodules from CT images.

Gopalakrishnan RC, Kuppusamy V - Comput Math Methods Med (2014)

Bottom Line: 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.The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

View Article: PubMed Central - PubMed

Affiliation: SCAD Institute of Technology, Palladam, Coimbatore 641664, India.

ABSTRACT
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.

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(a) Accuracy for 302 images. (b) Precision for 302 images for genetic algorithm based clustering.
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fig11: (a) Accuracy for 302 images. (b) Precision for 302 images for genetic algorithm based clustering.

Mentions: The image dataset has images with and without nodules. The precision and accuracy obtained using hierarchical agglomerative clustering algorithm are 63.7% and 64%, respectively. Figures 10(a) and 10(b) give the graph for accuracy and precision of hierarchical based clustering. Figure 11(a) gives accuracy for 302 images. For some images (image IDs 18, 25), the proposed algorithm has best clustering accuracy. This is because lung nodules were identified exactly and there were no improper identifications of lung nodules. This gives 100% accuracy for those images. The variation in accuracy is because for some lung images even the lung edges were identified as nodules. It contributes to the false negatives which subsequently reduces the accuracy. The accuracy for the all the images was calculated and the overall accuracy found to be 64%.


Ant colony optimization approaches to clustering of lung nodules from CT images.

Gopalakrishnan RC, Kuppusamy V - Comput Math Methods Med (2014)

(a) Accuracy for 302 images. (b) Precision for 302 images for genetic algorithm based clustering.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4265538&req=5

fig11: (a) Accuracy for 302 images. (b) Precision for 302 images for genetic algorithm based clustering.
Mentions: The image dataset has images with and without nodules. The precision and accuracy obtained using hierarchical agglomerative clustering algorithm are 63.7% and 64%, respectively. Figures 10(a) and 10(b) give the graph for accuracy and precision of hierarchical based clustering. Figure 11(a) gives accuracy for 302 images. For some images (image IDs 18, 25), the proposed algorithm has best clustering accuracy. This is because lung nodules were identified exactly and there were no improper identifications of lung nodules. This gives 100% accuracy for those images. The variation in accuracy is because for some lung images even the lung edges were identified as nodules. It contributes to the false negatives which subsequently reduces the accuracy. The accuracy for the all the images was calculated and the overall accuracy found to be 64%.

Bottom Line: 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.The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

View Article: PubMed Central - PubMed

Affiliation: SCAD Institute of Technology, Palladam, Coimbatore 641664, India.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus