In this paper,elitist reconstruction genetic algorithm(ERGA)based on Markov random field(MRF)is introduced for image segmentation.In this algorithm,a population of possible solutions is maintained at every generation,...In this paper,elitist reconstruction genetic algorithm(ERGA)based on Markov random field(MRF)is introduced for image segmentation.In this algorithm,a population of possible solutions is maintained at every generation,and for each solution a fitness value is calculated according to a fitness function,which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework.After the improved selection,crossover and mutation,an elitist individual is restructured based on the strategy of restructuring elitist.This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals.The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value.Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.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
In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and w...In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and what’s more, the brightness and contrast of the image are all poor. Using the traditional image segmentation method, the segmentation results are very poor. By adopting the maximum entropy and genetic algorithm, the maximum entropy function was used as the fitness function of genetic algorithm. Through continuous optimization, the optimal segmentation threshold is determined. Experimental results prove that the image segmentation of this paper not only fast and accurate, but also has strong adaptability.展开更多
Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich textur...Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM) . Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.展开更多
Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （...Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （CIR） aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm （VGA） for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm （BP）, it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, VGA classifier and BP-MLP （multi-layer perception） classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborh...Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.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
Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results ...Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results In our approach, the segmentation problem was formulated as an optimization problem and the fitness of GA which can efficiently search the segmentation parameter space was regarded as the quality criterion. Conclusion The methodcan be adapted for optimal behold segmentation.展开更多
Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PC...Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.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
In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to gui...In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.展开更多
Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for med...Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others.展开更多
This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-S...This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-SSA.The proposed method introduces a better search space to find the optimal solution at each iteration.However,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.The obtained solutions by the proposed method are represented using the image histogram.The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level.The performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking test.Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA.The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.展开更多
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decom...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
As watershed algorithm suffers from over-segmentation problem, this paper presented an efficient method to resolve this problem. First, pre-process of the image using median filter is made to reduce the effect of nois...As watershed algorithm suffers from over-segmentation problem, this paper presented an efficient method to resolve this problem. First, pre-process of the image using median filter is made to reduce the effect of noise. Second, watershed algorithm is employed to provide initial regions. Third, regions are merged according to the information between the region and boundary. In the merger processing based on the region information, an adaptive threshold of the difference between the neighboring regions is used as the region merge criteria, which is based on the human visual character. In the merger processing on the boundary information, the gradient is used to judge the true boundary of the image to avoid merging the foreground with the background regions. Finally, post-process to the regions using mathematical morphology open and close filter is done to smooth object boundaries. The experimental results show that this method is very efficient.展开更多
In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.Whe...In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.展开更多
A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the chara...A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.展开更多
To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventiona...To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.展开更多
In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal structures.CT image segmentation technology will effectiv...In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal structures.CT image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process,which is of great significance to the study of material performance.This study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT images.In the core layer area,area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed algorithm.In the transition layer area,a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation result.Furthermore,a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is proposed.It is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials,and the accuracy of segmentation is significantly improved in comparison with the original method,which ensures the accuracy and robustness of the subsequent fiber feature extraction process.展开更多
文摘In this paper,elitist reconstruction genetic algorithm(ERGA)based on Markov random field(MRF)is introduced for image segmentation.In this algorithm,a population of possible solutions is maintained at every generation,and for each solution a fitness value is calculated according to a fitness function,which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework.After the improved selection,crossover and mutation,an elitist individual is restructured based on the strategy of restructuring elitist.This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals.The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value.Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.
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.
文摘In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and what’s more, the brightness and contrast of the image are all poor. Using the traditional image segmentation method, the segmentation results are very poor. By adopting the maximum entropy and genetic algorithm, the maximum entropy function was used as the fitness function of genetic algorithm. Through continuous optimization, the optimal segmentation threshold is determined. Experimental results prove that the image segmentation of this paper not only fast and accurate, but also has strong adaptability.
基金Under the auspices of National Natural Science Foundation of China (No. 30370267)Key Project of Jilin Provincial Science & Technology Department (No. 20075014)
文摘Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM) . Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.
文摘Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （CIR） aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm （VGA） for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm （BP）, it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, VGA classifier and BP-MLP （multi-layer perception） classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
文摘Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.
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.
文摘Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results In our approach, the segmentation problem was formulated as an optimization problem and the fitness of GA which can efficiently search the segmentation parameter space was regarded as the quality criterion. Conclusion The methodcan be adapted for optimal behold segmentation.
基金This research is supported by the National Key Research and Development Program of China(2018YFB0804202,2018YFB0804203)Regional Joint Fund of NSFC(U19A2057),the National Natural Science Foundation of China(61672259,61876070)and the Jilin Province Science and Technology Development Plan Project(20190303134SF,20180201064SF).
文摘Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.
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.
文摘In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.
基金This research was supported by the Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others.
文摘This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-SSA.The proposed method introduces a better search space to find the optimal solution at each iteration.However,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.The obtained solutions by the proposed method are represented using the image histogram.The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level.The performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking test.Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA.The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
文摘As watershed algorithm suffers from over-segmentation problem, this paper presented an efficient method to resolve this problem. First, pre-process of the image using median filter is made to reduce the effect of noise. Second, watershed algorithm is employed to provide initial regions. Third, regions are merged according to the information between the region and boundary. In the merger processing based on the region information, an adaptive threshold of the difference between the neighboring regions is used as the region merge criteria, which is based on the human visual character. In the merger processing on the boundary information, the gradient is used to judge the true boundary of the image to avoid merging the foreground with the background regions. Finally, post-process to the regions using mathematical morphology open and close filter is done to smooth object boundaries. The experimental results show that this method is very efficient.
基金Science and Technology Plan of Gansu Province(No.144NKCA040)
文摘In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.
基金This project was supported by Science and Technology Research Emphasis Fund of Ministry of Education(204010) .
文摘A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley of the histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.
基金Project(20040533035) supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject (60874070) supported by the National Natural Science Foundation of China
文摘To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.
文摘In the study of the composite materials performance,X-ray computed tomography(XCT)scanning has always been one of the important measures to detect the internal structures.CT image segmentation technology will effectively improve the accuracy of the subsequent material feature extraction process,which is of great significance to the study of material performance.This study focuses on the low accuracy problem of image segmentation caused by fiber cross-section adhesion in composite CT images.In the core layer area,area validity is evaluated by morphological indicator and an iterative segmentation strategy is proposed based on the watershed algorithm.In the transition layer area,a U-net neural network model trained by using artificial labels is applied to the prediction of segmentation result.Furthermore,a CT image segmentation method for fiber composite materials based on the improved watershed algorithm and the U-net model is proposed.It is verified by experiments that the method has good adaptability and effectiveness to the CT image segmentation problem of composite materials,and the accuracy of segmentation is significantly improved in comparison with the original method,which ensures the accuracy and robustness of the subsequent fiber feature extraction process.
