Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

Abstract

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.

Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

“…Because of the classical ACM weaknesses discussed above, differential evolution is adopted to solve the local minima drawback by guiding the convergence of multiple active contours on a polar coordinate system similar to [16]. Since DE is directly applied in the segmentation task performed by MACDE, the advantages of robustness, low computational time, and efficiency are preserved.…”

“…The second drawback is the propensity to stagnate in local minima giving an inaccurate convergence to the boundaries of the object. To solve these disadvantages some improvements have been suggested to adapt different methods to work together with ACM including statistical methods [13, 14], graph cut [15], population based-methods such as particle swarm optimization (PSO) working with polar sections [16], static large searching windows [17] and by adapting the PSO velocity equation [18], genetic algorithms [19, 20], and differential evolution [21]. The performance of the population based-methods working together with ACM is very suitable according to the tests since the active contour becomes more stable, robust, and efficient in local minima problem.…”

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

“…PSO is a population-based computational intelligence technique, proposed to solve optimization problems [28]. The populations of PSO handle a set of randomly initialized solutions known as swarm that include a number of particles, each one represents potential solutions to the optimization task and moves iteratively through hyperspace to new positions according to the velocity equation as given in Equation 3.…”

“…This merit represents the main advantage with regard to other evaluatationally computation techniques. Additionally, PSO is not computationally expensive [28].…”

Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

“…Recent literature illustrates that the heuristic and metaheuristic algorithms such as particle swarm optimization (PSO) [20][21][22][23][24][25], bacterial foraging algorithm (BFO) [1,13,17,18], differential evaluation (DE) [19,[26][27][28], artificial bee colony (ABC) [11,29], cuckoo search (CS) [12,30], watershed algorithm [31], fuzzy logic [32], hybrid method [33], and self-adaptive parameter optimization algorithm [34] are widely considered for optimal multilevel image segmentation problem to enhance the outcome.…”