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
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
Watershed segmentation is sensitive to noises and irregular details within the image,which frequently leads to a serious over-segmentation Linear filtering before watershed segmentation can reduce over-segmentation to...Watershed segmentation is sensitive to noises and irregular details within the image,which frequently leads to a serious over-segmentation Linear filtering before watershed segmentation can reduce over-segmentation to some extent,however,it often causes the position offset of object contours.For the purpose of reducing over-segmentation to preserve the location of object contours,the watershed segmentation based on the hierarchical multi-scale modification of morphological gradient is proposed.Firstly,multi-scale morphological filtering was employed to smooth the original image.Then,the gradient image was divided into multi-levels by the volume of three-dimension topographic relief,where the lower gradient layers were further modifiedby morphological closing with larger-sized structuring-elements,and the higher layers with the smaller one.In this way,most local minimums caused by irregular details and noises can be removed,while region contour positions corresponding to the target area were largely preserved.Finally,morphological watershed algorithm was employed to implement segmentation on the modified gradient image.The experimental results show that the proposed method can greatly reduce the over-segmentation of the watershed and avoid the position offset of the object contours.展开更多
The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A waters...The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A watershed transformation technique is then employes. This includes: gradient of the classified image, dividing the image into markers, checking the Marker Image to see if it has zero points (watershed lines). The watershed lines are then deleted in the Marker Image created by watershed algorithm. A Region Adjacency Graph (RAG) and Region Adjacency Boundary (RAB) are created between two regions from Marker Image. Finally region merging is done according to region average intensity and two edge strengths (T1, T2). The approach of the authors is tested on remote sensing and brain MR medical images. The final segmentation result is one closed boundary per actual region in the image.展开更多
This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial esti...This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.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
In this paper, an elastic particle mesh （EPM） model is presented. It can be used like a cover to sketch images. EPM offers two advantages： first, when putting on a sketch image, it helps to repair disconnections on...In this paper, an elastic particle mesh （EPM） model is presented. It can be used like a cover to sketch images. EPM offers two advantages： first, when putting on a sketch image, it helps to repair disconnections on salient features. Second, it hides trivial details in the image, thus has the ability of decreasing over-segmentation when used with watershed transformation.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
Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic (CAD) system, especially on the segmentation between left and right lungs....Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic (CAD) system, especially on the segmentation between left and right lungs. Methods: We put forward the base frame of our lung segmentation firstly. Then, using optimal thresholding and mathematical morphologic methods, we acquired the rough image of lung segmentation. Finally, we presented a fast self-fit segmentation refinement algorithm, adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results: Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert’s manual outlines, and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion: It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.展开更多
Fingerprints are a unique feature for identification and verification of humans. The need to optimise several databases for storing the images of fingerprints is a major concerning issue. Several segmentation algorith...Fingerprints are a unique feature for identification and verification of humans. The need to optimise several databases for storing the images of fingerprints is a major concerning issue. Several segmentation algorithms have been used in the time past but there are still several challenges facing some current segmentation algorithms like computational efficiency. Another challenge is that segmentation procedure can be impractically slow, or requires extremely large amounts of memory. This paper addresses the challenges by employing watershed flooding algorithm on the fingerprint images so as to optimize the sizes of the databases. A pre-processing plug-in that implements this segmentation process is developed using Java. We showed its effectiveness by testing it on fingerprint image dataset and the entropy showed that the segmented images sizes were reduced.展开更多
The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This pap...The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.展开更多
This paper reports the quantitative study of martensite/austenite(M/A)constituents in high-grade heavy steel plates using the gradient-based watershed algorithm.Compared with several other image segmentation algorithm...This paper reports the quantitative study of martensite/austenite(M/A)constituents in high-grade heavy steel plates using the gradient-based watershed algorithm.Compared with several other image segmentation algorithms,the gradient-based watershed algorithm can effectively remove noises in images with various qualities and identify M/A constituents.The results prove that the gradient-based watershed algorithm is an effective method that can be used to digitize microstructures and integrate the data into Process Intelligent Data Application System(PIDAS).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
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National Natural Science Foundation of China(No.61261029)
文摘Watershed segmentation is sensitive to noises and irregular details within the image,which frequently leads to a serious over-segmentation Linear filtering before watershed segmentation can reduce over-segmentation to some extent,however,it often causes the position offset of object contours.For the purpose of reducing over-segmentation to preserve the location of object contours,the watershed segmentation based on the hierarchical multi-scale modification of morphological gradient is proposed.Firstly,multi-scale morphological filtering was employed to smooth the original image.Then,the gradient image was divided into multi-levels by the volume of three-dimension topographic relief,where the lower gradient layers were further modifiedby morphological closing with larger-sized structuring-elements,and the higher layers with the smaller one.In this way,most local minimums caused by irregular details and noises can be removed,while region contour positions corresponding to the target area were largely preserved.Finally,morphological watershed algorithm was employed to implement segmentation on the modified gradient image.The experimental results show that the proposed method can greatly reduce the over-segmentation of the watershed and avoid the position offset of the object contours.
文摘The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A watershed transformation technique is then employes. This includes: gradient of the classified image, dividing the image into markers, checking the Marker Image to see if it has zero points (watershed lines). The watershed lines are then deleted in the Marker Image created by watershed algorithm. A Region Adjacency Graph (RAG) and Region Adjacency Boundary (RAB) are created between two regions from Marker Image. Finally region merging is done according to region average intensity and two edge strengths (T1, T2). The approach of the authors is tested on remote sensing and brain MR medical images. The final segmentation result is one closed boundary per actual region in the image.
文摘This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.
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Project supported by National Natural Science Foundation of China（ Grant No. 60272081 ）
文摘In this paper, an elastic particle mesh （EPM） model is presented. It can be used like a cover to sketch images. EPM offers two advantages： first, when putting on a sketch image, it helps to repair disconnections on salient features. Second, it hides trivial details in the image, thus has the ability of decreasing over-segmentation when used with watershed transformation.
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.
基金the National Key Basic Research and Development Plan of China ("973" Projects, 2003CB716104)the Key Program of the National Natural Science Foundation of China (30730036)+1 种基金the Sci & Tech Planning Program of Guangdong Province (2007B010400058)the Sci & Tech Project Foundation of Guangzhou City (2007Z3-E0031)
文摘Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic (CAD) system, especially on the segmentation between left and right lungs. Methods: We put forward the base frame of our lung segmentation firstly. Then, using optimal thresholding and mathematical morphologic methods, we acquired the rough image of lung segmentation. Finally, we presented a fast self-fit segmentation refinement algorithm, adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results: Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert’s manual outlines, and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion: It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.
文摘Fingerprints are a unique feature for identification and verification of humans. The need to optimise several databases for storing the images of fingerprints is a major concerning issue. Several segmentation algorithms have been used in the time past but there are still several challenges facing some current segmentation algorithms like computational efficiency. Another challenge is that segmentation procedure can be impractically slow, or requires extremely large amounts of memory. This paper addresses the challenges by employing watershed flooding algorithm on the fingerprint images so as to optimize the sizes of the databases. A pre-processing plug-in that implements this segmentation process is developed using Java. We showed its effectiveness by testing it on fingerprint image dataset and the entropy showed that the segmented images sizes were reduced.
文摘The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.
文摘This paper reports the quantitative study of martensite/austenite(M/A)constituents in high-grade heavy steel plates using the gradient-based watershed algorithm.Compared with several other image segmentation algorithms,the gradient-based watershed algorithm can effectively remove noises in images with various qualities and identify M/A constituents.The results prove that the gradient-based watershed algorithm is an effective method that can be used to digitize microstructures and integrate the data into Process Intelligent Data Application System(PIDAS).
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.
