Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D

Image Enhancement Based on Rough Set and Fractional Order Differentiator

In the paper, an image enhancement algorithm based on a rough set and fractional order differentiator is proposed. By combining the rough set theory with a Gaussian mixture model, a new image segmentation algorithm with higher immunity is obtained. This image segmentation algorithm can obtain more image layers with concentrating information and preserve more image details than traditional algorithms. After preprocessing, the segmentation layers will be enhanced by a new adaptive fractional order differential mask in the Fourier domain. Experimental results and numerical analysis have verified the effectiveness of the proposed algorithm.

Adaptive Feature Weights Based Double-Layer Multi-Objective Method for SAR Image Segmentation

The recently proposed multi-objective clustering methods convert the segmentation problem to a multi-objective optimization problem by extracting multiple features from an image to be segmented as clustering data. However, most of these methods fail to consider the impacts of different features on segmentation results when calculating the similarity using the Euclidean distance. In this paper, feature domination is defined to segment the image efficiently, and then an adaptive feature weights based double-layer multi-objective method (AFWDLMO) for image segmentation is presented. The proposed method mainly contains two layers: a weight determination layer and a clustering layer. In the weight determination layer, AFWDLMO adaptively identifies the dominant feature of an image to be segmented and specifies its optimal weight through differential evolution. In the clustering layer, multi-objective clustering functions are established and optimized based on the acquired optimal weight, and a set of solutions with high segmentation accuracy is found. The segmentation results on several texture images and SAR images show that the proposed method is better than several existing state-of-the-art segmentation algorithms.

Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

Noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search and improved fuzzy c-means

Adding spatial penalty to fuzzy C-means (FCM) model is an important way to reduce the influence of noise in image segmentation. However, these improved algorithms easily cause segmentation failures when the image has the characteristics of unequal cluster sizes. Besides, they often fall into local optimal solutions if the initial cluster centers are improper. This paper presents a noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search algorithm and improved fuzzy c-means to overcome the above defects. Firstly, each size of clusters is integrated into the objective function of noise detecting fuzzy c-means algorithm (NDFCM), which can reduces the contribution of larger clusters to objective function and then the new membership degree and cluster centers are deduced. Secondly, a new expression called compactness, representing the pixel distribution of each cluster, is introduced into the iteration process of clustering. Thirdly, we use two- paths to seek the optimal solutions in each step of iteration: one path is produced by the chaotic crow search algorithm and the other is originated by gradient method. Furthermore, the better solutions of the two-paths go to next generation until the end of the iteration. Finally, the experiments on the synthetic and non–destructive testing (NDT) images show that the proposed algorithm behaves well in noise robustness and segmentation performance.

Segmentation of MR Brain Images Through Hidden Markov Random Field and Hybrid Metaheuristic Algorithm

Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.

A multi-objective approach for designing optimized operation sequence on binary image processing

In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image, in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost. We compared the performance of two Multi-objective Evolutionary Algorithms (MOEAs): the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). NSGA-II has better results in most cases, but the difference does not reach statistical significance. The results show that the similarity measure and the computational cost are objective functions in conflict, while the number of operations available and type of input images impact on the quality of Pareto set.

A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods

In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known complementary techniques, called fuzzy entropy clustering method and region-based active contour method, using multi-objective particle swarm optimization (MOPSO) approach. In order to obtain accurate segmentation results, firstly, two fitness functions with independent characteristics, compactness and separation, are derived from kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) and a novel adaptive energy weight combined with global and local fitting energy active contour (AWGLAC) model. Then, they are simultaneously optimized to finally produce a set of non-dominated solutions, from which L₂-metric method is used to select the best trade-off solution. Our algorithm is both verified and compared with other state-of-the-art methods using simulated MR images and real MR images from the McConnell Brain Imaging Center (BrainWeb) and the Internet Brain Segmentation Repository (IBSR), respectively. The experimental results demonstrate that the proposed technique achieves superior segmentation performance in terms of accuracy and robustness.