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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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Logical ant colony optimization algorithm.
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alg6: Logical ant colony optimization algorithm.

Mentions: From the refined Ant Colony Optimization algorithm's output and normal Ant Colony Optimization algorithm's output, we notice that it detects noises along with the nodules. Hence a logical operation is applied to get even better detection of lung nodules. We get the final iteration output of refined ACO and the previous iteration output of refined ACO algorithm and then apply XOR to it to get the logical ACO output. This shows further reduction of noises in the output image. Figures 6(a) and 6(b) are the input to logical ACO and Figure 6(c) shows the output after applying logical ACO. The detailed pseudocode is given in Algorithm 6.


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

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

Logical ant colony optimization algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

alg6: Logical ant colony optimization algorithm.
Mentions: From the refined Ant Colony Optimization algorithm's output and normal Ant Colony Optimization algorithm's output, we notice that it detects noises along with the nodules. Hence a logical operation is applied to get even better detection of lung nodules. We get the final iteration output of refined ACO and the previous iteration output of refined ACO algorithm and then apply XOR to it to get the logical ACO output. This shows further reduction of noises in the output image. Figures 6(a) and 6(b) are the input to logical ACO and Figure 6(c) shows the output after applying logical ACO. The detailed pseudocode is given in Algorithm 6.

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