Color Image Segmentation Method Research Articles

A colour image segmentation method and its application to medical images

A colour image segmentation method and its application to medical images

Fusion-based color and depth image segmentation method for rocks on conveyor belt

Semi-autogenous mill (SAG) feeding process requires accurate and reliable monitoring and controlling techniques for multi-sized rock distribution to stabilize crushing productivity. This study presents a color and depth (RGB-D) image fusion algorithm for the segmentation of multiscale rock images from the conveyor belt. The algorithm combines the multiscale characteristics of the color image and depth image, considers the extraction of large particles from the depth image and the rich texture details for segmentation of the color image, divides the segmentation regions according to different particle sizes, and finally obtains the contour of each particle after region-based superpixels merging. The proposed method outperforms any single sensor image segmentation method proposed in direct comparison, thereby attaining the closest to ground truth performance. The comparison results also show that the accuracy and adaptability of the algorithm are significantly improved, and this algorithm can therefore be used in rock-like material segmentation with multiple size distribution on conveyors.

An efficient multilevel thresholding segmentation method based on improved chimp optimization algorithm

To improve the traditional image segmentation, an efficient multilevel thresholding segmentation method based on improved Chimp Optimization Algorithm (IChOA) is developed in this paper. Kapur entropy is utilized as the objective function. The best threshold values for RGB images’ three channels are found using IChOA. Meanwhile, several strategies are introduced including population initialization strategy combining with Gaussian chaos and opposition-based learning, the position update mechanism of particle swarm algorithm (PSO), the Gaussian-Cauchy mutation and the adaptive nonlinear strategy. These methods enable the IChOA to raise the diversity of the population and enhance both the exploration and exploitation. Additionally, the search ability, accuracy and stability of IChOA have been significantly enhanced. To prove the superiority of the IChOA based multilevel thresholding segmentation method, a comparison experiment is conducted between IChOA and 5 six meta-heuristic algorithms using 12 test functions, which fully demonstrate that IChOA can obtain high-quality solutions and almost does not suffer from premature convergence. Furthermore, by using 10 standard test images the IChOA-based multilevel thresholding image segmentation method is compared with other peers and evaluated the segmentation results using 5 evaluation indicators with the average fitness value, PSNR, SSIM, FSIM and computational time. The experimental results reveal that the presented IChOA-based multilevel thresholding image segmentation method has tremendous potential to be utilized as an image segmentation method for color images because it can be an effective swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of color images.

An improved mayfly algorithm based on Kapur entropy for multilevel thresholding color image segmentation

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images.

A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation

A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation

Circular Histogram Breakpoint Selection and Threshold and Color Image Segmentation Method Based on Information Energy

Circular Histogram Breakpoint Selection and Threshold and Color Image Segmentation Method Based on Information Energy

Convolution Optimized Density Clustering Algorithm for Image Segmentation

In order to improve the usability of DBSCAN in the field of high-resolution image segmentation, according to the characteristics of digital images, an efficient DBSCAN based on three-dimensional convolution is proposed, and on this basis, a color image segmentation method is implemented. First, convolution is introduced into DBSCAN, three-dimensional convolution is used to obtain the core object, avoiding distance calculation and region retrieval, and reducing the time complexity of this step from the second order to a constant level. Then, the three-dimensional convolution is optimized based on dynamic programming, so that the convolution time is independent of the size of the convolution kernel. Finally, the neighborhood search range of the sample points is simplified according to the ordered core object set. Compared with the existing algorithms, the experimental results on the Berkeley BSD300 show that the algorithm is effective, the CH index and the time efficiency increase by 17% and 49% on average. The results of experiments using the remote sensing image set Mts-WH show that the computational efficiency of the algorithm is significantly improved. Compared with the DBSCAN optimized by the <italic>k</italic>D tree, the efficiency is increased by an average of 19 times, and the improvement effect is more obvious as the number of sample points increases. When the number of points exceeds 500 000, the efficiency is increased by 169 times.

An effective color image segmentation approach using superpixel-neutrosophic C-means clustering and gradient-structural similarity

Due to the insufficient expression of uncertain information in fuzzy sets, the fuzzy C-means clustering algorithm is difficult to deal with clustered boundary and outliers. Therefore, this paper proposes a color image segmentation method based on neutrosophic C-means clustering. First, we improved the simple linear clustering algorithm to obtain accurate and natural adaptive local spatial neighborhoods. Secondly, local neighborhood information is added to the objective function of neutrosophic C-means clustering to obtain a more accurate membership. According to the membership, superpixels are divided into certainty group and uncertainty group. Finally, the certainty group is classified by the maximum membership, and the superpixels of the uncertain group are classified by the structural similarity. Experimental results show that the proposed method has better performance on clean and noise images.

CGFFCM: A color image segmentation method based on cluster-weight and feature-weight learning

<h2>Abstract</h2> CGFFCM (Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means) is a clustering-based color image segmentation approach. It applies an automatic cluster weighting strategy to mitigate the initialization sensitivity and a group-local feature weighting technique to improve the clustering accuracy. In addition, it exploits an efficient combination of image features, consisting of eight features from three different groups (i.e., local homogeneity, CIELAB color space, and texture), to increase the image segmentation quality. CGFFCM also utilizes the imperialist competitive algorithm to optimize its feature weighting process. An open-source Matlab implementation of CGFFCM is available.

An unsupervised segmentation method based on dynamic threshold neural P systems for color images

Dynamic threshold neural P (DTNP) systems are a new variant of spiking neural P (SNP) systems, abstracted by the spiking and dynamic threshold mechanisms of biological neurons. Motivated by the two mechanisms, this paper develops a novel segmentation method for color images. For this purpose, a 2-dimentional DTNP system with local topology is designed. The spiking mechanism of neurons is used to achieve regional growth, and dynamic threshold is considered to realize the choice of seeds. Moreover, local weights that combine color information can control the regional growth. The proposed segmentation method is evaluated on two benchmark Berkeley segmentation data sets (BSD300 and BSD500) and is compared with 17 state-of-the-art segmentation methods. Experimental results demonstrate the availability and effectiveness of the proposed segmentation method for color images.

HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation

Traditional methods to address color image segmentation work efficiently for bi-level thresholding. However, for multi-level thresholding, traditional methods suffer from time complexity that increases exponentially with the increasing number of threshold levels. To overcome this problem, in this paper, a new approach is proposed to tackle multi-threshold color image segmentation by employing the Otsu method as an objective function. This approach is based on a hybrid of the whale optimization algorithm (WOA) with a novel method called the local minima avoidance method (LMAM), abbreviated as HWOA. LMAM avoids local minima by updating the whale either within the search space of the problem or between two whales selected randomly from the population-based on a certain probability. HWOA is validated on ten color images taken from the Berkeley University Dataset by measuring the objective values, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), features similarity index (FSIM), and CPU time, and compared with a number of the well-known robust meta-heuristic algorithms: the sine–cosine algorithm (SCA), WOA, modified salp swarm algorithm (MSSA), improved marine predators algorithm (IMPA), modified Cuckoo Search (CS) using McCulloch’s algorithm (CSMC), and equilibrium optimizer (EO). The experimental results show that HWOA is superior to all the other algorithms in terms of PSNR, FSIM, and objective values, and is competitive in terms of SSIM.

Three-dimensional thermohydrodynamic analysis of the effects of textured main bearing on lubricant density and viscosity in internal combustion engines

This paper is focused on the effects of the textured surface along with lubricant feed pressure and temperature variation to calculate the lubrication characteristics (viscosity and density) of the main bearings in internal combustion engines. In a three-dimensional numerical analysis simulation, a thermohydrodynamic model was developed, which considered the thermal effects of the fluid–solid interface components. In the analysis, the viscosity–temperature equation was used at different lubricant feed pressures and temperatures. By using color image segmentation methods, the density and viscosity contour on the two rotating walls (shaft and bearing wall) in the cavitation regions were quantitatively and qualitatively compared. The textured surface and lubricant feed pressure had significant effects on reducing the cavitation region over the shaft and bearing surface. Compared with the untextured bearing, the presence of texture over the bearing surface in the same temperature and pressure conditions increased the amount of total heat transfer to the lubricant fluid in the range of the maximum value of 73% and minimum value of 11%. Promoting the average heat transfer coefficient was a factor in reducing thermal stresses and high-temperature points in the rigid parts of the bearing system.

Recursive Algorithms of Maximum Entropy Thresholding on Circular Histogram

Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria.

A Type-2 Fuzzy Clustering and Quantum Optimization Approach for Crops Image Segmentation

Automatic detection of crop yield ripeness is a tedious task because of the presence of various intensities of color in crops. One of the solutions to this problem is the monitoring of those crops by performing segmentation operations. This operation can help to distinguish the ripe and non-ripe regions among the crop images. For this purpose, this study presents a new hybrid crop image segmentation method utilizing type-2 fuzzy set (T2FS), K-means clustering algorithm, and modified quantum optimization algorithm (MQOA). The proposed method fully utilizes the indispensable qualities of these three techniques by (a) using T2FS to represent each color component of crop images in terms of secondary memberships, (b) applying K-means clustering algorithm to extract the similar features from the set of type-2 entropy values obtained from the secondary memberships, and (c) exploiting MQOA to optimize the distance function used in K-means clustering algorithm to obtain the optimal clusters. The performance of the proposed method is assessed based on the experiments carried out on color images of cherry tomatoes. Evidence of experimental results suggests that the proposed method produces extremely effective segmented images relative to those well-known color image segmentation methods available in the literature of pattern recognition and computer vision domains.

A hybrid equilibrium optimizer algorithm for multi-level image segmentation.

Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA.

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

Superpixel Based Hierarchical Segmentation for Color Image

In this letter, we propose a hierarchical segmentation (HS) method for color images, which can not only maintain the segmentation accuracy, but also ensure a good speed. In our method, HS adopts the fuzzy simple linear iterative clustering (Fuzzy SLIC) to obtain an over-segmentation result. Then, HS uses the fast fuzzy C-means clustering (FFCM) to produce the rough segmentation result based on superpixels. Finally, HS takes the non-iterative K-means clustering using priority queue (KPQ) to refine the segmentation result. In the validation experiments, we tested our method and compared it with state-of-the-art image segmentation methods on the Berkeley (BSD500) benchmark under different types of noise. The experiment results show that our method outperforms state-of-the-art techniques in terms of accuracy, speed and robustness.

RGB-D image segmentation using superpixel and multi-feature fusion graph theory

It is difficult to obtain accurate segmentation results for a color map when there are shadows, low-contrast edges, or blurred regions in the image. The depth discontinuity of the image provides useful information for the identification of object boundaries. In this paper, a color and depth image (RGB-D) image segmentation method based on superpixels and multi-feature fusion graph theory is proposed. The method consists of two stages: (1) superpixel segmentation stage and (2) graph-based superpixel merge stage. Color information and depth information are combined to establish a Euclidean distance metric for the pixels, and superpixels are obtained through iterative clustering. A multi-feature fusion adjacent superpixel similarity measurement method based on the RGB-D data is proposed. A graph-based energy function is established, and a label cost is introduced in the energy function to eliminate redundant labels. We carried out many experiments on the RGB-D image database, and the results show that our superpixel segmentation method and graph-based superpixel merging method not only have higher segmentation accuracy than the existing methods, but also have advantages in terms of running time and memory consumption.

An improved emperor penguin optimization based multilevel thresholding for color image segmentation

This paper proposes a multi-threshold image segmentation method based on improved emperor penguin optimization (EPO). The calculation complexity of multi-thresholds increases with the increase of the number of thresholds. To overcome this problem, the EPO is used to find the optimal multilevel threshold values for color images. Then, the Gaussian mutation, the Levy flight and the opposition-based learning are employed to increase the search ability of EPO algorithm and balance the exploitation and exploration. The IEPO algorithm optimizes the Kapur’s multi-threshold method to conduct experiments on Berkeley images, Satellite images and plant canopy images. As the experimental results show, the IEPO is the effective method for color image segmentation and have higher segmentation accuracy and less CPU time.

An Integrated Color Image Segmentation with Multi-class SVM followed by SRFCM

In existing the segmentation of a color image is mostly depends on the features color , texture or on both color and texture but proposed method for color image segmentation is based on both color and texture with multi-class SVM (Support Vector Machine).For color feature extraction we used homogeneity model and for textural features we used PLD (Power Law Descriptor). With the help of SR-FCM (Soft Rough Fuzzy-C-Means) clustering. Membership functions based on the fuzzy set are facing the major problem of cluster overlapping. The rough set concepts can help us to get correct data from incomplete data, uncertainty of data. For defining the soft set theory there is no any requirement of parameterization tools. To get improved results of proposed algorithm the combination of aspects of fuzzy sets, rough sets as well as soft sets are used. The feature extraction for textural feature is done by using spatial domain which helps to reduce the run time complexity. Proposed method provides better performance which is compared with all the state of art techniques which is developed and analyzed using MATLAB.