Abstract
Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.
Highlights
The lung tissue is composed by alveolus, bronchial tubes and blood vessels
We have tested fifteen 3D lung computed tomography (CT) cases, and there are more than 500 continuous images in each case
The quantitative assessment shows that the proposed ant colony optimization (ACO) method achieved the average F-measures of
Summary
The lung tissue is composed by alveolus, bronchial tubes and blood vessels. Because the densities of air in alveolus and bronchial tubes are lower and cannot pass through by X-ray, the Hounsfield unit (HU) is lower in CT images as shown for the gray regions [1,2]. It may cause the segmentation faults for the lobes separation in clinical experiments.
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