Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ...Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.展开更多
Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Trans...Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.展开更多
To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cau...To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.展开更多
In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussi...In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial discretization.Compared to the standard radial basis functionmethod,this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain.Additionally,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic images.Finally,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.展开更多
Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as ...Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and conditions.Due to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning networks.The aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation resources.With the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer learning.Moreover,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique.The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources.The segmentation results are evaluated using 14 clinical 3D brain MR and CT images.The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±0.0007.Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.展开更多
Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in ...Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in 2015.However,the application of deep learning models to ocular medical image segmentation poses unique challenges,especially compared to other body parts,due to the complexity,small size,and blurriness of such images,coupled with the scarcity of data.This article aims to provide a comprehensive review of medical image segmentation from two perspectives:the development of deep network structures and the application of segmentation in ocular imaging.Initially,the article introduces an overview of medical imaging,data processing,and performance evaluation metrics.Subsequently,it analyzes recent developments in U-Net-based network structures.Finally,for the segmentation of ocular medical images,the application of deep learning is reviewed and categorized by the type of ocular tissue.展开更多
Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance inte...Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance interdependence, which limits the segmentation performance. Transformer has been successfully applied to various computer vision, using self-attention mechanism to simulate long-distance interaction, so as to capture global information. However, self-attention lacks spatial location and high-performance computing. In order to solve the above problems, we develop a new medical transformer, which has a multi-scale context fusion function and can be used for medical image segmentation. The proposed model combines convolution operation and attention mechanism to form a u-shaped framework, which can capture both local and global information. First, the traditional converter module is improved to an advanced converter module, which uses post-layer normalization to obtain mild activation values, and uses scaled cosine attention with a moving window to obtain accurate spatial information. Secondly, we also introduce a deep supervision strategy to guide the model to fuse multi-scale feature information. It further enables the proposed model to effectively propagate feature information across layers, Thanks to this, it can achieve better segmentation performance while being more robust and efficient. The proposed model is evaluated on multiple medical image segmentation datasets. Experimental results demonstrate that the proposed model achieves better performance on a challenging dataset (ETIS) compared to existing methods that rely only on convolutional neural networks, transformers, or a combination of both. The mDice and mIou indicators increased by 2.74% and 3.3% respectively.展开更多
Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical ima...Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical imaging. With the recent advances in deep learning (DL) and itsconfounding results in image segmentation, more attention has been drawnto its use in medical image segmentation. This article introduces a surveyof the state-of-the-art deep convolution neural network (CNN) models andmechanisms utilized in image segmentation. First, segmentation models arecategorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed withineach category. Compared with other existing surveys, several applicationswith multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilizedarchitectures in different applications and datasets. This study focuses onmedical image segmentation applications, where the most widely used architecturesare illustrated, and other promising models are suggested that haveproven their success in different domains. Finally, the present work discussescurrent limitations and solutions along with future trends in the field.展开更多
As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so ...As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so on.Compared with natural images,medical images have a variety of modes.Besides,the emphasis of information which is conveyed by images of different modes is quite different.Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors.Therefore,large quantities of automated medical image segmentation methods have been developed.However,until now,researchers have not developed a universal method for all types of medical image segmentation.This paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent years.Among the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation methods.One is the improved strategies based on traditional clustering method.The other is the research progress of the improved image segmentation network structure model based on U-Net.The power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional method.This paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.展开更多
An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image s...An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.展开更多
In medical image segmentation task,convolutional neural networks(CNNs)are difficult to capture long-range dependencies,but transformers can model the long-range dependencies effectively.However,transformers have a fle...In medical image segmentation task,convolutional neural networks(CNNs)are difficult to capture long-range dependencies,but transformers can model the long-range dependencies effectively.However,transformers have a flexible structure and seldom assume the structural bias of input data,so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training.To solve these problems,a dual branch structure is proposed.In one branch,Mix-Feed-Forward Network(Mix-FFN)and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model.Mix-FFN whose depth-wise convolutions can provide position information is better than ordinary positional encoding.In the other branch,traditional convolutional neural networks(CNNs)are used to extract different features of fewer medical images.In addition,the attention fusion module BiFusion is used to effectively integrate the information from the CNN branch and Transformer branch,and the fused features can effectively capture the global and local context of the current spatial resolution.On the public standard datasets Gland Segmentation(GlaS),Colorectal adenocarcinoma gland(CRAG)and COVID-19 CT Images Segmentation,the F1-score,Intersection over Union(IoU)and parameters of the proposed TC-Fuse are superior to those by Axial Attention U-Net,U-Net,Medical Transformer and other methods.And F1-score increased respectively by 2.99%,3.42%and 3.95%compared with Medical Transformer.展开更多
In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dime...In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.展开更多
Aiming at the problems of inaccurate edge segmentation,the hole phenomenon of segmenting large-scale targets,and the slow segmentation speed of printed circuit boards(PCB)in the image segmentation process,a PCB image ...Aiming at the problems of inaccurate edge segmentation,the hole phenomenon of segmenting large-scale targets,and the slow segmentation speed of printed circuit boards(PCB)in the image segmentation process,a PCB image segmentation model Mobile-Deep based on DeepLabv3+semantic segmentation framework is proposed.Firstly,the DeepLabv3+feature extraction network is replaced by the lightweight model MobileNetv2,which effectively reduces the number of model parameters;secondly,for the problem of positive and negative sample imbalance,a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve the model discriminative ability;in addition,a more efficient atrous spatial pyramid pooling(E-ASPP)module is proposed.In addition,a more efficient E-ASPP module is proposed,and the Roberts crossover operator is chosen to sharpen the image edges to improve the model accuracy;finally,the network structure is redesigned to further improve the model accuracy by drawing on the multi-scale feature fusion approach.The experimental results show that the proposed segmentation algorithm achieves an average intersection ratio of 93.45%,a precision of 94.87%,a recall of 93.65%,and a balance score of 93.64%on the PCB test set,which is more accurate than the common segmentation algorithms Hrnetv2,UNet,PSPNet,and PCBSegClassNet,and the segmentation speed is faster.展开更多
Electrical trees are an aging mechanismmost associated with partial discharge(PD)activities in crosslinked polyethylene(XLPE)insulation of high-voltage(HV)cables.Characterization of electrical tree structures gained c...Electrical trees are an aging mechanismmost associated with partial discharge(PD)activities in crosslinked polyethylene(XLPE)insulation of high-voltage(HV)cables.Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material.Two-dimensional(2D)optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods.However,since electrical trees can emerge in different shapes such as bush-type or branch-type,treeing images are complicated to segment due to manifestation of convoluted tree branches,leading to a high misclassification rate during segmentation.Therefore,this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm(MSLTA)by integrating batch processing method.The proposed method,h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy.The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation.The treeing images are then sampled and binarized in pre-processing.In the next phase,segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration.Finally,the comparative investigation has been conducted using standard performance assessment metrics,including accuracy,sensitivity,specificity,Dice coefficient and Matthew’s correlation coefficient(MCC).Based on segmentation performance evaluation against several established segmentation methods,h-MSLTA achieved better results of 95.43%accuracy,97.28%specificity,69.43%sensitivity rate with 23.38%and 24.16%average improvement in Dice coefficient and MCC score respectively over the original algorithm.In addition,h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image.These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.展开更多
Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the im...Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.展开更多
In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segme...In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.展开更多
In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate ...In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in clustering.We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise.Built on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise.Besides,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image itself.Supporting experiments on synthetic,medical,and real-world images are conducted.The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.展开更多
In the methods of image thresholding segmentation, such methods based on two-dimensional (2D) histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, the...In the methods of image thresholding segmentation, such methods based on two-dimensional (2D) histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions (i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy) respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms' deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.展开更多
Ore image segmentation is a key step in an ore grain size analysis based on image processing.The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer...Ore image segmentation is a key step in an ore grain size analysis based on image processing.The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation.In this article,in order to solve the problem,an ore image segmentation method based on U-Net is proposed.We adjust the structure of U-Net to speed up the processing,and we modify the loss function to enhance the generalization of the model.After the collection of the ore image,we design the annotation standard and train the network with the annotated image.Finally,the marked watershed algorithm is used to segment the adhesion area.The experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high precision.It has great practical value to the actual ore grain statistical task.展开更多
In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid betwee...In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.展开更多
基金financially supported by the National Key Research and Development Program(Grant No.2022YFE0107000)the General Projects of the National Natural Science Foundation of China(Grant No.52171259)the High-Tech Ship Research Project of the Ministry of Industry and Information Technology(Grant No.[2021]342)。
文摘Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.
基金supported by the National Key R&D Program of China(2018AAA0102100)the National Natural Science Foundation of China(No.62376287)+3 种基金the International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province(2021CB1013)the Key Research and Development Program of Hunan Province(2022SK2054)the Natural Science Foundation of Hunan Province(No.2022JJ30762,2023JJ70016)the 111 Project under Grant(No.B18059).
文摘Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.
基金This work is supported by Natural Science Foundation of Anhui under Grant 1908085MF207,KJ2020A1215,KJ2021A1251 and 2023AH052856the Excellent Youth Talent Support Foundation of Anhui underGrant gxyqZD2021142the Quality Engineering Project of Anhui under Grant 2021jyxm1117,2021kcszsfkc307,2022xsxx158 and 2022jcbs043.
文摘To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.
基金sponsored by Guangdong Basic and Applied Basic Research Foundation under Grant No.2021A1515110680Guangzhou Basic and Applied Basic Research under Grant No.202102020340.
文摘In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial discretization.Compared to the standard radial basis functionmethod,this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain.Additionally,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic images.Finally,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.
基金Swiss National Science Foundation,Grant/Award Number:SNSF 320030_176052Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung,Grant/Award Number:320030_176052。
文摘Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body.The segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and conditions.Due to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning networks.The aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation resources.With the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer learning.Moreover,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique.The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources.The segmentation results are evaluated using 14 clinical 3D brain MR and CT images.The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±0.0007.Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.
文摘Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in 2015.However,the application of deep learning models to ocular medical image segmentation poses unique challenges,especially compared to other body parts,due to the complexity,small size,and blurriness of such images,coupled with the scarcity of data.This article aims to provide a comprehensive review of medical image segmentation from two perspectives:the development of deep network structures and the application of segmentation in ocular imaging.Initially,the article introduces an overview of medical imaging,data processing,and performance evaluation metrics.Subsequently,it analyzes recent developments in U-Net-based network structures.Finally,for the segmentation of ocular medical images,the application of deep learning is reviewed and categorized by the type of ocular tissue.
文摘Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish long-distance interdependence, which limits the segmentation performance. Transformer has been successfully applied to various computer vision, using self-attention mechanism to simulate long-distance interaction, so as to capture global information. However, self-attention lacks spatial location and high-performance computing. In order to solve the above problems, we develop a new medical transformer, which has a multi-scale context fusion function and can be used for medical image segmentation. The proposed model combines convolution operation and attention mechanism to form a u-shaped framework, which can capture both local and global information. First, the traditional converter module is improved to an advanced converter module, which uses post-layer normalization to obtain mild activation values, and uses scaled cosine attention with a moving window to obtain accurate spatial information. Secondly, we also introduce a deep supervision strategy to guide the model to fuse multi-scale feature information. It further enables the proposed model to effectively propagate feature information across layers, Thanks to this, it can achieve better segmentation performance while being more robust and efficient. The proposed model is evaluated on multiple medical image segmentation datasets. Experimental results demonstrate that the proposed model achieves better performance on a challenging dataset (ETIS) compared to existing methods that rely only on convolutional neural networks, transformers, or a combination of both. The mDice and mIou indicators increased by 2.74% and 3.3% respectively.
基金supported by the Information Technology Industry Development Agency (ITIDA),Egypt (Project No.CFP181).
文摘Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical imaging. With the recent advances in deep learning (DL) and itsconfounding results in image segmentation, more attention has been drawnto its use in medical image segmentation. This article introduces a surveyof the state-of-the-art deep convolution neural network (CNN) models andmechanisms utilized in image segmentation. First, segmentation models arecategorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed withineach category. Compared with other existing surveys, several applicationswith multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilizedarchitectures in different applications and datasets. This study focuses onmedical image segmentation applications, where the most widely used architecturesare illustrated, and other promising models are suggested that haveproven their success in different domains. Finally, the present work discussescurrent limitations and solutions along with future trends in the field.
基金supported partly by the Open Project of State Key Laboratory of Millimeter Wave under Grant K202218partly by Innovation and Entrepreneurship Training Program of College Students under Grants 202210700006Y and 202210700005Z.
文摘As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so on.Compared with natural images,medical images have a variety of modes.Besides,the emphasis of information which is conveyed by images of different modes is quite different.Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors.Therefore,large quantities of automated medical image segmentation methods have been developed.However,until now,researchers have not developed a universal method for all types of medical image segmentation.This paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent years.Among the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation methods.One is the improved strategies based on traditional clustering method.The other is the research progress of the improved image segmentation network structure model based on U-Net.The power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional method.This paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.
基金Supported by the Research and Development Project of Experimental Technology,China University of Mining and Technology(Study on mineral occurrence in coal based on SEM and EDS,S2023Y018)the National Natural Science Foundations of China under Grant 62371451.
文摘An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.
基金supported in part by the National Natural Science Foundation of China under Grant 61972267the National Natural Science Foundation of Hebei Province under Grant F2018210148+1 种基金the University Science Research Project of Hebei Province under Grant ZD2021334the Science and Technology Project of Hebei Education Department(ZD2022098).
文摘In medical image segmentation task,convolutional neural networks(CNNs)are difficult to capture long-range dependencies,but transformers can model the long-range dependencies effectively.However,transformers have a flexible structure and seldom assume the structural bias of input data,so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training.To solve these problems,a dual branch structure is proposed.In one branch,Mix-Feed-Forward Network(Mix-FFN)and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model.Mix-FFN whose depth-wise convolutions can provide position information is better than ordinary positional encoding.In the other branch,traditional convolutional neural networks(CNNs)are used to extract different features of fewer medical images.In addition,the attention fusion module BiFusion is used to effectively integrate the information from the CNN branch and Transformer branch,and the fused features can effectively capture the global and local context of the current spatial resolution.On the public standard datasets Gland Segmentation(GlaS),Colorectal adenocarcinoma gland(CRAG)and COVID-19 CT Images Segmentation,the F1-score,Intersection over Union(IoU)and parameters of the proposed TC-Fuse are superior to those by Axial Attention U-Net,U-Net,Medical Transformer and other methods.And F1-score increased respectively by 2.99%,3.42%and 3.95%compared with Medical Transformer.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 62171203in part by the Suzhou Key Supporting Subjects[Health Informatics(No.SZFCXK202147)]+2 种基金in part by the Changshu Science and Technology Program[No.CS202015,CS202246]in part by the Changshu City Health and Health Committee Science and Technology Program[No.csws201913]in part by the“333 High Level Personnel Training Project of Jiangsu Province”.
文摘In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.
基金funded by the University-Industry Cooperation Project“Research and Application of Intelligent Traveling Technology for Steel Logistics Based on Industrial Internet”,Grant Number 2022H6005Natural Science Foundation of Fujian Provincial Science and Technology Department,Grant Number 2022J01952Research Start-Up Projects,Grant Number GY-Z12079.
文摘Aiming at the problems of inaccurate edge segmentation,the hole phenomenon of segmenting large-scale targets,and the slow segmentation speed of printed circuit boards(PCB)in the image segmentation process,a PCB image segmentation model Mobile-Deep based on DeepLabv3+semantic segmentation framework is proposed.Firstly,the DeepLabv3+feature extraction network is replaced by the lightweight model MobileNetv2,which effectively reduces the number of model parameters;secondly,for the problem of positive and negative sample imbalance,a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve the model discriminative ability;in addition,a more efficient atrous spatial pyramid pooling(E-ASPP)module is proposed.In addition,a more efficient E-ASPP module is proposed,and the Roberts crossover operator is chosen to sharpen the image edges to improve the model accuracy;finally,the network structure is redesigned to further improve the model accuracy by drawing on the multi-scale feature fusion approach.The experimental results show that the proposed segmentation algorithm achieves an average intersection ratio of 93.45%,a precision of 94.87%,a recall of 93.65%,and a balance score of 93.64%on the PCB test set,which is more accurate than the common segmentation algorithms Hrnetv2,UNet,PSPNet,and PCBSegClassNet,and the segmentation speed is faster.
基金the Ministry of Higher Education Malaysia for financially supported under the FundamentalResearch Grant Scheme (FRGS/1/2020/TK0/UNIMAP/02/17).
文摘Electrical trees are an aging mechanismmost associated with partial discharge(PD)activities in crosslinked polyethylene(XLPE)insulation of high-voltage(HV)cables.Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material.Two-dimensional(2D)optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods.However,since electrical trees can emerge in different shapes such as bush-type or branch-type,treeing images are complicated to segment due to manifestation of convoluted tree branches,leading to a high misclassification rate during segmentation.Therefore,this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm(MSLTA)by integrating batch processing method.The proposed method,h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy.The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation.The treeing images are then sampled and binarized in pre-processing.In the next phase,segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration.Finally,the comparative investigation has been conducted using standard performance assessment metrics,including accuracy,sensitivity,specificity,Dice coefficient and Matthew’s correlation coefficient(MCC).Based on segmentation performance evaluation against several established segmentation methods,h-MSLTA achieved better results of 95.43%accuracy,97.28%specificity,69.43%sensitivity rate with 23.38%and 24.16%average improvement in Dice coefficient and MCC score respectively over the original algorithm.In addition,h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image.These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.
基金supported by the National Natural Science Foundation of China(6087403160740430664)
文摘Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.
基金supported by the National Natural Science Foundation of China (31501229)the Chinese Academy of Agricultural Sciences Innovation Project (CAAS-ASTIP2017-AII)the Special Research Funds for Basic Scientific Research in Central Public Welfare Research Institutes, China (JBYW-AII-2017-05)
文摘In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.
基金supported in part by the Doctoral Students’Short Term Study Abroad Scholarship Fund of Xidian Universitythe National Natural Science Foundation of China(61873342,61672400,62076189)+1 种基金the Recruitment Program of Global Expertsthe Science and Technology Development Fund,MSAR(0012/2019/A1)。
文摘In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in clustering.We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise.Built on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise.Besides,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image itself.Supporting experiments on synthetic,medical,and real-world images are conducted.The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.
基金supported by the China Postdoctoral Science Foundation(20100471451)the Science and Technology Foundation of State Key Laboratory of Underwater Measurement&Control Technology(9140C2603051003)
文摘In the methods of image thresholding segmentation, such methods based on two-dimensional (2D) histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions (i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy) respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms' deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.
基金This work was supported by The National Natural Science Foundation of China(Grant 61801019).
文摘Ore image segmentation is a key step in an ore grain size analysis based on image processing.The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation.In this article,in order to solve the problem,an ore image segmentation method based on U-Net is proposed.We adjust the structure of U-Net to speed up the processing,and we modify the loss function to enhance the generalization of the model.After the collection of the ore image,we design the annotation standard and train the network with the annotated image.Finally,the marked watershed algorithm is used to segment the adhesion area.The experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high precision.It has great practical value to the actual ore grain statistical task.
文摘In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.