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 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
Objective: To propose a method to segment tongue- images efficiently, and extract tongue- body accurately and quickly. Methods: Firstly, a kind of color- images' pre- processing technique was used to solve tongue-...Objective: To propose a method to segment tongue- images efficiently, and extract tongue- body accurately and quickly. Methods: Firstly, a kind of color- images' pre- processing technique was used to solve tongue- surface reflection problem. Neighbouring and similar region's information was used to restore the region with tongue- surface reflection problem by replacement. Secondly, the restored image was transformed into a gray one, and then processed by mathematical morphological operation- dilation to get a closed- loop edge. The third technique used was watershed algorithm, which is an usual tool in image segmentation. 'Watershed' function of matlab software was used to complete this algorithm. After that, region- combination technique was used. Through measuring neighbourship and similarity of regions, a non- objective and non- background region was merged into one of its neighbouring regions. This step was repeated until only two regions, objective and background regions, were left in the image. At last, corresponding to the merged image, tongue- body image was got from the original image. Results: 316 images were randomly taken from the image library for experiments, and 299 images were correctly segmented, so, the successful ratio is 94.62%. On the other hand, average time of running this method was about 50 s under whole sampling environment. Conclusion: The method presented in this paper can segment a tongue- body image from its original one effectively, and thus laying a good foundation for the following analysis work.展开更多
Aiming at the problem that the traditional watershed image segmentation algorithm is sensitive to noise and prone to ＂over-segmentation＂, an image segmentation method based on improved watershed algorithm is propose...Aiming at the problem that the traditional watershed image segmentation algorithm is sensitive to noise and prone to ＂over-segmentation＂, an image segmentation method based on improved watershed algorithm is proposed. First, the denoising method is used to denoise the tea image by using the differential equation denoising model The interference of the image on the image segmentation, the protection of the tea image of the edge of the tea information; and then use the watershed algorithm to denoise the tea image after the split. The simulation results show that this method can effectively avoid the influence of noise on image segmentation, and get a good image of ,louug leaves of tea image.展开更多
Accurate measurement of bean particle size is essential for automated grading and quality control in agricultural processing.However,existing image segmentation methods often suffer from low efficiency,over-segmentati...Accurate measurement of bean particle size is essential for automated grading and quality control in agricultural processing.However,existing image segmentation methods often suffer from low efficiency,over-segmentation,and high computational cost.We proposed a distancegradient dual constrained watershed algorithm for precise segmentation and measurement of bean particles.The method integrated distance transform-based seed extraction with gradient-constrained flooding,effectively suppressing noise-induced region fragmentation and improving the separation of adherent particles.An experimental platform was constructed using an industrial camera and an image-processing pipeline to evaluate performance.Compared with the conventional watershed algorithm,the proposed method improves segmentation accuracy by 7.2%and reduces the mean particle size error by 27.8%(0.13 mm,representing a relative error of 2.4%).Validation on three soybean varieties confirmed the robustness and generalizability of the approach.The results indicated that the proposed algorithm provided an efficient and accurate technique for agricultural particle size analysis,offering potential for integration into practical low-cost inspection systems.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
Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address t...Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.展开更多
To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was proposed.Firstly,on the basis of morphological gradient information,...To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was proposed.Firstly,on the basis of morphological gradient information,obtained by comparing color weight gradient images and proposing a multi-threshold segmentation,scene contour features were extracted by a watershed algorithm and a fuzzy c-means clustering algorithm.Secondly,to reduce the search area,increase the correct matching ratio and accelerate the matching speed,the region constraint was established according to a region＇s local position,area and gray characteristics,the edge pixel constraint was established according to the epipolar constraint and the continuity constraint.Finally,by using the stereo matching edge pixel pairs,their 3D coordinates were estimated according to the binocular stereo vision imaging model.Experimental results show that the proposed method can yield a high stereo matching ratio and reconstruct a 3D scene quickly and efficiently.展开更多
This paper presents an algorithm of automatic bubble image segmentation using the improved ant colony optimization methodology. The ant colony optimization method is a metaheuristic algorithm, and has been applied in ...This paper presents an algorithm of automatic bubble image segmentation using the improved ant colony optimization methodology. The ant colony optimization method is a metaheuristic algorithm, and has been applied in many fields. To reveal the versatility and appropriateness of automatic bubble image segmentation, the fuzzy clustering analysis method is employed in ant colony optimization algorithm. Compared with the well-known image feature extraction operators such as SUSAN and Canny, the proposed method can comparatively suitable to extract the gas bubbles image edge features. The experimental results show that the proposed method is effective and reliable, and can achieve satisfactory image edge extraction effect.展开更多
Particle size distribution is extremely important in the coal preparation industry.It is traditionally analysed by a manual screening method,which is relatively time-consuming and cannot immediately guide production.I...Particle size distribution is extremely important in the coal preparation industry.It is traditionally analysed by a manual screening method,which is relatively time-consuming and cannot immediately guide production.In this paper,an image segmentation method for images of coal particles is proposed.It employs the watershed algorithm,k-nearest neighbour algorithm,and convex shell method to achieve preliminary segmentation,merge small pieces with large pieces,and split adhered particles,respectively.Comparing the automated segmentation using this method with manual segmentation,it is found that the results are comparable.The size distributions obtained by the automated and manual segmentation methods are nearly identical,and the standard deviation is less than 3%,indicating good reliability.This automated image segmentation method provides a new approach for rapidly analysing the size distribution of coal particles with size fractions defined according to consumer requirements.展开更多
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.
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.
基金National Natural Science Foundation of China grant number: 30371717
文摘Objective: To propose a method to segment tongue- images efficiently, and extract tongue- body accurately and quickly. Methods: Firstly, a kind of color- images' pre- processing technique was used to solve tongue- surface reflection problem. Neighbouring and similar region's information was used to restore the region with tongue- surface reflection problem by replacement. Secondly, the restored image was transformed into a gray one, and then processed by mathematical morphological operation- dilation to get a closed- loop edge. The third technique used was watershed algorithm, which is an usual tool in image segmentation. 'Watershed' function of matlab software was used to complete this algorithm. After that, region- combination technique was used. Through measuring neighbourship and similarity of regions, a non- objective and non- background region was merged into one of its neighbouring regions. This step was repeated until only two regions, objective and background regions, were left in the image. At last, corresponding to the merged image, tongue- body image was got from the original image. Results: 316 images were randomly taken from the image library for experiments, and 299 images were correctly segmented, so, the successful ratio is 94.62%. On the other hand, average time of running this method was about 50 s under whole sampling environment. Conclusion: The method presented in this paper can segment a tongue- body image from its original one effectively, and thus laying a good foundation for the following analysis work.
文摘Aiming at the problem that the traditional watershed image segmentation algorithm is sensitive to noise and prone to ＂over-segmentation＂, an image segmentation method based on improved watershed algorithm is proposed. First, the denoising method is used to denoise the tea image by using the differential equation denoising model The interference of the image on the image segmentation, the protection of the tea image of the edge of the tea information; and then use the watershed algorithm to denoise the tea image after the split. The simulation results show that this method can effectively avoid the influence of noise on image segmentation, and get a good image of ,louug leaves of tea image.
基金supported by National Natural Science Foundation of China(No.62006092)University Synergy Innovation Program of Anhui Province(No.GXXT-2023-108)Excellent Youth Project of Natural Science Research in Anhui Province(No.2023AH030081).
文摘Accurate measurement of bean particle size is essential for automated grading and quality control in agricultural processing.However,existing image segmentation methods often suffer from low efficiency,over-segmentation,and high computational cost.We proposed a distancegradient dual constrained watershed algorithm for precise segmentation and measurement of bean particles.The method integrated distance transform-based seed extraction with gradient-constrained flooding,effectively suppressing noise-induced region fragmentation and improving the separation of adherent particles.An experimental platform was constructed using an industrial camera and an image-processing pipeline to evaluate performance.Compared with the conventional watershed algorithm,the proposed method improves segmentation accuracy by 7.2%and reduces the mean particle size error by 27.8%(0.13 mm,representing a relative error of 2.4%).Validation on three soybean varieties confirmed the robustness and generalizability of the approach.The results indicated that the proposed algorithm provided an efficient and accurate technique for agricultural particle size analysis,offering potential for integration into practical low-cost inspection systems.
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.
基金the Project of China Scholarship Council(No.201708615011)the Xi’an Science and Technology Plan Project(No.GXYD1.7)。
文摘Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.
基金Supported by the Ministerial Level Advanced Research Foundation(40401060305)
文摘To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was proposed.Firstly,on the basis of morphological gradient information,obtained by comparing color weight gradient images and proposing a multi-threshold segmentation,scene contour features were extracted by a watershed algorithm and a fuzzy c-means clustering algorithm.Secondly,to reduce the search area,increase the correct matching ratio and accelerate the matching speed,the region constraint was established according to a region＇s local position,area and gray characteristics,the edge pixel constraint was established according to the epipolar constraint and the continuity constraint.Finally,by using the stereo matching edge pixel pairs,their 3D coordinates were estimated according to the binocular stereo vision imaging model.Experimental results show that the proposed method can yield a high stereo matching ratio and reconstruct a 3D scene quickly and efficiently.
基金Sponsored by the"Liaoning Bai Qian Wan"Talents Program (Grant No.2007-186-25)the Program of Scientific Research Project of Liaoning Province Education Commission (Grant No.LS2010046)the National Commonweal Industry Scientific Research Project (Grant No.201003024)
文摘This paper presents an algorithm of automatic bubble image segmentation using the improved ant colony optimization methodology. The ant colony optimization method is a metaheuristic algorithm, and has been applied in many fields. To reveal the versatility and appropriateness of automatic bubble image segmentation, the fuzzy clustering analysis method is employed in ant colony optimization algorithm. Compared with the well-known image feature extraction operators such as SUSAN and Canny, the proposed method can comparatively suitable to extract the gas bubbles image edge features. The experimental results show that the proposed method is effective and reliable, and can achieve satisfactory image edge extraction effect.
文摘Particle size distribution is extremely important in the coal preparation industry.It is traditionally analysed by a manual screening method,which is relatively time-consuming and cannot immediately guide production.In this paper,an image segmentation method for images of coal particles is proposed.It employs the watershed algorithm,k-nearest neighbour algorithm,and convex shell method to achieve preliminary segmentation,merge small pieces with large pieces,and split adhered particles,respectively.Comparing the automated segmentation using this method with manual segmentation,it is found that the results are comparable.The size distributions obtained by the automated and manual segmentation methods are nearly identical,and the standard deviation is less than 3%,indicating good reliability.This automated image segmentation method provides a new approach for rapidly analysing the size distribution of coal particles with size fractions defined according to consumer requirements.
