改进的粒子群算法多模态生物医学图像配准

多模态生物医学图像配准在医疗诊断、治疗方案的制定,以及身体机能的研究等方面起到越来越大的作用。如何将这些多模态信息融合在一起是目前研究的重点,目前,该融合主要基于图像强度信息的配准方法。该类方法通过最大化化图像间的相似度函数达到配准的目的,但配准过程中使用往往会出现参数变化非凸且不光滑的现象,因而,传统的局部最优方法通常不能得到较好的结果。粒子群算法是一种全局寻优算法,但传统的方法中受初始值的选取以及当前全局最优点的影响,易陷入局部最优。本文对其进行改进,使得即使在初始值离准确值较远时也能得到全局最优,并将该方法用于多模态医学图像配准中,得到了较好的结果。     

改进收敛条件的动态调整惯性权重PSO算法

在医学图像配准中需要解决互信息图像配准过程中局部极值问题,引入了一种动态调整惯性权的自适应粒子群算法;验证了其中两个重要参数的取值,并均匀赋值粒子初始位置,避免随机产生的初始位置集中在某一区域而使寻优陷入局部极值,同时加入进化速度因子作为搜索中止条件,加快了搜索速度。实验表明,该算法既能找到全局最优又能快速收敛。     

Affine-based registration of CT and MR modality images of human brain using multiresolution approaches: comparative study on genetic algorithm and particle swarm optimization

We present a non-linear 2-D/2-D affine registration technique for MR and CT modality images of section of human brain. Automatic registration is achieved by maximization of a similarity metric, which is the correlation function of two images. The proposed method has been implemented by choosing a realistic, practical transformation and optimization techniques. Correlation-based similarity metric should be maximal when two images are perfectly aligned. Since similarity metric is a non-convex function and contains many local optima, choice of search strategy for optimization is important in registration problem. Many optimization schemes are existing, most of which are local and require a starting point. In present study we have implemented genetic algorithm and particle swarm optimization technique to overcome this problem. A comparative study shows the superiority and robustness of swarm methodology over genetic approach.     

An improved quantum-behaved particle swarm optimization and its application to medical image registration

This paper investigates the quantum-behaved particle swarm optimization (QPSO) algorithm from the perspective of estimation of distribution algorithm (EDA) which reveals the reason of QPSO's superiority. A revised QPSO (RQPSO) technique with a novel iterative equation is also proposed. The modified technique is deduced from the distribution function of the sum of two random variables with exponential and normal distribution, respectively. We present a diversity-controlled RQPSO (DRQPSO) algorithm, which helps prevent the evolutionary algorithms’ tendency to be easily trapped into local optima as a result of rapid decline in diversity. Both the RQPSO and DRQPSO are tested on three benchmark functions, as well as in medical image registration for performance comparison with the particle swarm optimization and QPSO.     

Metaheuristic methods in hybrid flow shop scheduling problem

Memetic algorithms are hybrid evolutionary algorithms that combine global and local search by using an evolutionary algorithm to perform exploration while the local search method performs exploitation. This paper presents two hybrid heuristic algorithms that combine particle swarm optimization (PSO) with simulated annealing (SA) and tabu search (TS), respectively. The hybrid algorithms were applied on the hybrid flow shop scheduling problem. Experimental results reveal that these memetic techniques can effectively produce improved solutions over conventional methods with faster convergence.     

多层次降维的头颈癌图像特征选择方法

针对原始病理图像经软件提取形态学特征后存在高维度,以及医学领域上样本的少量性问题,提出ReliefF-HEPSO头颈癌病理图像特征选择算法。该算法构建了多层次降维框架,首先根据特征和类别的相关性,利用ReliefF算法确定不同的特征权重,实现初步降维。其次利用进化神经策略(ENS)丰富二进制粒子群算法(BPSO)的种群的多样性,提出混合二进制进化粒子群算法(HEPSO)对候选特征子集完成最佳特征子集的自动寻找。与7种特征选择算法的实验对比结果证明,该算法能更有效筛选出高相关性的病理图像形态学特征,实现快速降维,以较少特征获得较高分类性能。     

An image contrast enhancement algorithm for grayscale images using particle swarm optimization

This paper addresses a contrast enhancement technique that combines classical contrast enhancement with an evolutionary approach. The central goal of this work is to increase the information content and enhance the details of an image using an adaptive gamma correction technique aided by particle swarm optimization. Gamma correction is a well established technique that preserves the mean brightness of an image that produces natural looking images by the choice of an optimal gamma value. Here, Swarm intelligence based particle swarm optimization is employed to estimate an optimal gamma value. In the proposed method, the edge and information content (entropy) are the parameters used to formulate the fitness function. The proposed method is compared with state-of-the-art of techniques in terms of Weighted Average Peak Signal to Noise Ratio (WPSNR), Contrast, Homogeneity, Contrast Noise Ratio (CNR), and Measure of Enhancement (EME). Simulation results demonstrate that the proposed particle swarm optimization based contrast enhancement method improves the overall image contrast and enriches the information present in the image. In comparison to other contrast enhancement techniques, the proposed method brings out the hidden details of an image and is more suitable for applications in satellite imaging and night vision.     

Quality time-of-flight range imaging for feature-based registration using bacterial foraging

Image registration is a widely tackled research topic in the computer vision and the computer graphics fields. This problem aims to find an optimal transformation or correspondence between images acquired under different conditions. Recently, a new 3D image acquisition device based on the time-of-flight technology has appeared which obtains range images from real-time 3D video sequences. In this contribution, we aim to study the feasibility of using this new class of cameras to face the 3D model reconstruction procedure. Our proposal is two-fold. First, we introduce a novel image preprocessing pipeline in order to improve the quality of time-of-flight range images and a subsequent feature extraction method considering both 2D and 3D images. As second major objective, we propose an adaptation of the evolutionary bacterial foraging optimization algorithm, which has recently emerged as a very powerful technique for real parameter optimization and gained a high interest for distributed optimization and control, to tackle the range image registration problem. Finally, we analyse the performance of our proposal against other state-of-the-art evolutionary image registration methods.     

改进混合策略的多分辨率3D医学图像配准

基于混合策略的多分辨率算法是当前3D医学图像刚体配准中普遍采用的方法,不过其仅仅是优化算法的混合。通过研究不同分辨率对一阶互信息（常称为互信息）和二阶互信息配准的影响,在二级多分辨率策略的配准中,各级采用相对更适合的相似性测度,提出了混合优化算法和混合测度的改进算法。实验表明,改进算法在配准精度上达到了亚体素级,且明显优于基于单一测度的算法,在配准速度上远远快于基于二阶互信息单一测度的算法,略慢于基于一阶互信息单一测度的算法。     

Hybrid approach to implement multi-robotic navigation system using neural network,fuzzy logic,and bio-inspired optimization methodologies

Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi-robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta-heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi-robotics to travel from the initial point to such goal. The fundamental purpose of portable multi-robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio-inspired methods that are exploited to optimize hybrid neuro-fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the particle swarm optimization technique in real-time scenarios. Several optimization approaches of bio-inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi-robot navigation accuracy in terms of navigation duration and length of the path.     

基于分块互信息和量子粒子群算法的图像配准

多模图像的配准是图像融合等图像处理需要先行解决的问题.本文提出了一种基于分块互信息和量子粒子群的配准方法,在配准中利用分块互信息值为相似性测度,并用量子粒子群算法求解配准所需的空间变换参数.实验表明:该方法能够避免陷入局部极小值而且速度得到明显改善,其运用于多模图像配准,可以得到理想的效果.     

Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM

This paper proposes two novel soft and evolutionary computing based hybrid data association techniques to track multiple targets in the presence of electronic countermeasures (ECM), clutter and false alarms. Joint probabilistic data association (JPDA) approach is generally used for tracking multiple targets. Fuzzy clustering means (FCM) technique was proposed earlier as an efficient method for data association, but its cluster centers may fall to local minima. Hence, new hybrid data association approaches based on fuzzy particle swarm optimization (Fuzzy-PSO) and fuzzy genetic algorithm (Fuzzy-GA) clustering techniques have been presented as robust methods to overcome local minima problem. The data association matrix is evaluated for all tracks using validated measurements obtained by phased array radar for four different cases applying four data association methods (JPDA, FCM, Fuzzy-PSO, and Fuzzy-GA). Therefore, two hybrid data association approaches are designed and tested for multi-target tracking using intelligent techniques. Experimental results indicate that Fuzzy-GA data association technique provides improved performance compared to all other methods in terms of position and velocity RMSE values (38.69% and 33.19% average improvement for target-1;31.17% and 9.68% average improvement for target-2) respectively for crossing linear targets case. However, FCM technique gives better performance in terms of execution time (94.88% less average execution time) in comparison with other three techniques(JPDA, Fuzzy-GA, and Fuzzy-PSO) for the case of linear crossing targets. Thus accomplishing efficient and alternative multiple target tracking algorithms based on expert systems. The results have been validated with 100 Monte Carlo runs.     

A hybrid PSO-GD based intelligent method for machine diagnosis

This paper presents an intelligent methodology for diagnosing incipient faults in rotating machinery. In this fault diagnosis system, wavelet neural network techniques are used in combination with a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of the constriction factor approach for particle swarm optimization (PSO) technique and the gradient descent (GD) technique, and is thus called HGDPSO. The HGDPSO is developed in such a way that a constriction factor approach for particle swarm optimization (CFA for PSO) is applied as a based level search, which can give a good direction to the optimal global region, and a local search gradient descent (GD) algorithm is used as a fine tuning to determine the optimal solution at the final. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity.     

Medical image registration based on self-adapting pulse-coupled neural networks and mutual information

Medical image registration plays a dominant role in medical image analysis and clinical research. In this paper, we present a new coarse-to-fine method based on pulse-coupled neural networks (PCNNs) and mutual information (MI). In the coarse-registration process, we use the PCNN-clusters’ invariant characteristics of translation, rotation and distortion to get the coarse parameters. And the parameters of the PCNN model are optimized by ant colony optimization algorithm. In the fine-registration process, the coarse parameters provide a near-optimal initial solution. Based on this, the fine-tuning process is implemented by mutual information using the particle swarm optimization algorithm to search the optimal parameters. For the purpose of proving the proposed method can deal with medical image registration automatically, the experiments are carried out on MR and CT images. The comparative experiments on MI-based and SIFT-based methods for medical image registration show that the proposed method achieves higher performance in accuracy.     

粒子群优化小波变换的图像数据库相似性搜索

有许多图像检索系统能对图像数据库进行快速的相似性搜索,但利用这些图像搜索方法所得到的结果质量是相当有限的。本文中,首先引入几种一般的用于图像数据库相似性搜索的算法,利用这些算法形成可适应不同转换的模型,再基于这个模型并借助于演化计算的有效性,构造出一个适应度的函数,该函数利用小波变换通过粒子群优化算法使图像相似性搜索得到最优化。仿真实验结果验证了采用这种方法所得结果比其他相似性搜索方法质量更好。     

Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma

This paper presents and investigates the application of coevolutionary training techniques based on particle swarm optimization (PSO) to evolve playing strategies for the nonzero sum problem of the iterated prisoner's dilemma (IPD). Three different coevolutionary PSO techniques are used, differing in the way that IPD strategies are presented: A neural network (NN) approach in which the NN is used to predict the next action, a binary PSO approach in which the particle represents a complete playing strategy, and finally, a novel approach that exploits the symmetrical structure of man-made strategies. The last technique uses a PSO algorithm as a function approximator to evolve a function that characterizes the dynamics of the IPD. These different PSO approaches are compared experimentally with one another, and with popular man-made strategies. The performance of these approaches is evaluated in both clean and noisy environments. Results indicate that NNs cooperate well, but may develop weak strategies that can cause catastrophic collapses. The binary PSO technique does not have the same deficiency, instead resulting in an overall state of equilibrium in which some strategies are allowed to exploit the population, but never dominate. The symmetry approach is not as successful as the binary PSO approach in maintaining cooperation in both noisy and noiseless environments-exhibiting selfish behavior against the benchmark strategies and depriving them of receiving almost any payoff. Overall, the PSO techniques are successful at generating a variety of strategies for use in the IPD, duplicating and improving on existing evolutionary IPD population observations.     

Chaotic species based particle swarm optimization algorithms and its application in PCB components detection

An improved particle swarm optimizer using the notion of chaos and species is proposed for solving a template matching problem which is formulated as a multimodal optimization problem. Template matching is one of the image comparison techniques. This technique is widely applied to determine the existence, location and alignment of a component within a captured image in the printed circuit board (PCB) industry where 100% quality assurance is always required. In this research, an efficient auto detection method using a multiple templates matching technique for PCB components detection is described. The new approach using chaotic species based particle swarm optimization (SPSO) is applied to the multi-template matching (MTM) process. To test its performance, the proposed Chaotic SPSO based MTM algorithm is compared with other approaches by using real captured PCB images. The Chaotic SPSO based MTM method is proven to be superior to other methods in both efficiency and effectiveness.     

Human mental search-based multilevel thresholding for image segmentation

Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel thresholding are time-consuming computationally, especially when the number of thresholds increases. Use of evolutionary algorithms has attracted a lot of attention under such circumstances. Human mental search algorithm is a population-based evolutionary algorithm inspired by the manner of human mental search in online auctions. This algorithm has three interesting operators: (1) clustering for finding the promising areas, (2) mental search for exploring the surrounding of every solution using Levy distribution, and (3) moving the solutions toward the promising area. In the present study, multilevel thresholding is proposed for image segmentation using human mental search algorithm. Kapur (entropy) and Otsu (between-class variance) criteria were used for this purpose. The advantages of the proposed method are described using twelve images and in comparison with other existing approaches, including genetic algorithm, particle swarm optimization, differential evolution, firefly algorithm, bat algorithm, gravitational search algorithm, and teaching-learning-based optimization. The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarity index, and the curse of dimensionality. In addition, two nonparametric statistical tests verified the efficiency of the proposed algorithm, statistically.     

AUTOMATIC FLOWER BOUNDARY EXTRACTION USING IPSOAntK-MEANS ALGORITHM

Automatic flower boundary extraction is an important part of flower image recognition and retrieval. Identifying a flower of interest or segmenting against the background is a difficult task. We proposed and developed a hybrid automatic flower boundary extraction method called IPSOAntK-means based on swarm intelligence techniques in this article. The method employs ant colony optimization, incremental particle swarm optimization (IPSO), and K-means algorithms and it is the first swarm-based technique in flower segmentation on color images. The parameters of the algorithm are tuned by iterated F-race and experiments are conducted over two different benchmark data sets: CAVIAR-Flower and Oxford 17 flowers data sets. In the CAVIAR-flower data set, IPSOAntK-means outperformed K-means with 96.4% accuracy on average over 600 colored flower images. Comparison results of the Oxford flower data set show that our method is one of the best flower segmentation methods in the literature.