The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images

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.

Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

“…To better suppress tubular structures, Lassen et al [5], Shamonin et al [6] and Doel et al [7] added one or more tubular structure inhibitors. Using a different strategy, Chen et al [8] introduced a modified ant colony optimization framework to identify lobe fissures. Because the prior linear and planar shape features are not taken into account, these approaches may partly account for missed fissures.…”

Research on an Improved Multi-Population Ant Colony Optimization Algorithm and its Application

“…Zhu and Wang [27] proposed a modified ant colony optimization algorithm to solve the virtual network embedding (VNE) problem. Chen et al [28] proposed a modified ant colony optimization (ACO) algorithm to develop an efficient tracking framework to extract the lobe fissures.…”