Limits...
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.

Show MeSH

Related in: MedlinePlus

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


getmorefigures.php?uid=PMC4265538&req=5

alg1: Otsu algorithm.

Mentions: Otsu uses gray scale images for its image processing steps. Hence we take a gray scale lung CT images for further processing. In this method it searches for the pixels with all possible threshold values and finds the spread of pixels in each threshold range. It involves finding the pixels that fall under foreground and background. The edge is detected when the sum of foreground and background spread is the maximum. The mean weight and variance are calculated. Then within class variance is calculated whose value is used to detect the edge (refer to Algorithm 1). Figures 1(a) and 1(b) show the input-output of Otsu edge detection.


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

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

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

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

alg1: Otsu algorithm.
Mentions: Otsu uses gray scale images for its image processing steps. Hence we take a gray scale lung CT images for further processing. In this method it searches for the pixels with all possible threshold values and finds the spread of pixels in each threshold range. It involves finding the pixels that fall under foreground and background. The edge is detected when the sum of foreground and background spread is the maximum. The mean weight and variance are calculated. Then within class variance is calculated whose value is used to detect the edge (refer to Algorithm 1). Figures 1(a) and 1(b) show the input-output of Otsu edge detection.

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