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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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Related in: MedlinePlus

Watershed algorithm.
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Related In: Results  -  Collection


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alg2: Watershed algorithm.

Mentions: In grey scale images, different grey levels indicate the edges. Watershed algorithm basically sees the image as topographic relief. The basic idea behind this is construction of dams. The catchment areas refer to the objects we are trying to segment; here the catchment areas are the lung nodules. As the water level increases, dams are constructed to protect ourselves. When the water level reaches the highest peak construction stops. In the same way, we start from the watershed pixels and grow iteratively. When the edge detected reaches the maximum level, the process stops and gives the required edges. The detailed procedure is given in Algorithm 2. Figure 2(b) represents the edge detection of watershed algorithm for Figure 2(a).


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

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

Watershed algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

alg2: Watershed algorithm.
Mentions: In grey scale images, different grey levels indicate the edges. Watershed algorithm basically sees the image as topographic relief. The basic idea behind this is construction of dams. The catchment areas refer to the objects we are trying to segment; here the catchment areas are the lung nodules. As the water level increases, dams are constructed to protect ourselves. When the water level reaches the highest peak construction stops. In the same way, we start from the watershed pixels and grow iteratively. When the edge detected reaches the maximum level, the process stops and gives the required edges. The detailed procedure is given in Algorithm 2. Figure 2(b) represents the edge detection of watershed algorithm for Figure 2(a).

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