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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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Global region based segmentation algorithm.
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alg3: Global region based segmentation algorithm.

Mentions: The lung fields are segmented in CT image using a region growing algorithm. The algorithm is based on the selection of pixel; the pixel can be selected either by giving (x, y) coordinate or by clicking a pixel from the CT image. After selecting the pixel, the regions associated with this pixel based on connectivity and gray scale difference were formed by using the region mean. Through this method the given CT images were segmented and lung nodule edges are detected. The procedure of global region based segmentation is given in Algorithm 3. For Figure 3(a), the input image, the edge detection by global region based segmentation is shown in Figure 3(b).


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

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

Global region based segmentation algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

alg3: Global region based segmentation algorithm.
Mentions: The lung fields are segmented in CT image using a region growing algorithm. The algorithm is based on the selection of pixel; the pixel can be selected either by giving (x, y) coordinate or by clicking a pixel from the CT image. After selecting the pixel, the regions associated with this pixel based on connectivity and gray scale difference were formed by using the region mean. Through this method the given CT images were segmented and lung nodule edges are detected. The procedure of global region based segmentation is given in Algorithm 3. For Figure 3(a), the input image, the edge detection by global region based segmentation is shown in Figure 3(b).

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