Segmenting the lesion regions from the ultrasound (US) images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound (HIFU), as a popular computer-...Segmenting the lesion regions from the ultrasound (US) images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound (HIFU), as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons: (1) the blurry or missing boundaries of lesion regions in the HIFU images and (2) the deformation of uterine fibroids caused by the patient's breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier (ALM). Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.展开更多
Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar(SAR) images. A novel method is proposed based on integrating the geometric active contour(GAC) and the sup...Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar(SAR) images. A novel method is proposed based on integrating the geometric active contour(GAC) and the support vector machine(SVM)models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items: the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.展开更多
A fast two-stage geometric active contour algorithm for image segmentation is developed. First, the Eikonal equation problem is quickly solved using an improved fast sweeping method, and a criterion of local minimum o...A fast two-stage geometric active contour algorithm for image segmentation is developed. First, the Eikonal equation problem is quickly solved using an improved fast sweeping method, and a criterion of local minimum of area gradient (LMAG) is presented to extract the optimal arrival time. Then, the final time function is passed as an initial state to an area and length minimizing flow model, which adjusts the interface more accurately and prevents it from leaking. For object with complete and salient edge, using the first stage only is able to obtain an ideal result, and this results in a time complexity of O(M), where M is the number of points in each coordinate direction. Both stages are needed for convoluted shapes, but the computation cost can be drastically reduced. Efficiency of the algorithm is verified in segmentation experiments of real images with different feature.展开更多
Gradient vector flow (GVF) is an effective external force for active contours, but its iso- tropic nature handicaps its performance. The recently proposed gradient vector flow in the normal direction (NGVF) is ani...Gradient vector flow (GVF) is an effective external force for active contours, but its iso- tropic nature handicaps its performance. The recently proposed gradient vector flow in the normal direction (NGVF) is anisotropic since it only keeps the diffusion along the normal direction of the isophotes; however, it has difficulties forcing a snake into long, thin boundary indentations. In this paper, a novel external force for active contours called normally generalized gradient vector flow (NGGVF) is proposed, which generalizes the NGVF formulation to include two spatially varying weighting functions. Consequently, the proposed NGGVF snake is anisotropic and would improve ac- tive contour convergence into long, thin boundary indentations while maintaining other desirable properties of the NGVF snake, such as enlarged capture range, initialization insensitivity and good convergence at concavities. The advantages on synthetic and real images are demonstrated.展开更多
In the present study, a generalized active contour model of gradient vector flow is combined with the video techniques of Argus system to delineate and track sequential nearshore wave crest profiles in the shoaling pr...In the present study, a generalized active contour model of gradient vector flow is combined with the video techniques of Argus system to delineate and track sequential nearshore wave crest profiles in the shoaling process, up to their breaking on the shoreline. Previous applications of active contour models to water wave problems are limited to controllable wave tank experiments. By contrast, our application in this study is in a nearshore field environment where oblique images obtained under natural and varying condition of ambient light are employed. Existing Argus techniques produce plane image data or time series data from a selected small subset of discrete pixels. By contrast, the active contour model produces line image data along continuous visible curves such as wave crest profiles. The combination of these two existing techniques, the active contour model and Argus methodologies, facilitates the estimates of the direction wave field and phase speeds within the whole area covered by camera. These estimates are useful for the purpose of inverse calculation of the water depth. Applications of the present techniques to Hsi-tzu bay where a beach restoration program is currently undertaken are illustrated. This extension of Argus video techniques provides new application of optical remote sensing to study the hydrodynamics and morphology of a nearshore environment.展开更多
A novel active contour model is proposed, which incorporates local information distributions in a fuzzy energy function to effectively deal with the intensity inhomogeneity. Moreover, the proposed model is convex with...A novel active contour model is proposed, which incorporates local information distributions in a fuzzy energy function to effectively deal with the intensity inhomogeneity. Moreover, the proposed model is convex with respect to the variable which is used for extracting the contour. This makes the model independent on the initial condition and suitable for an automatic segmentation. Furthermore, the energy function is minimized in a computationally efficient way by calculating the fuzzy energy alterations directly. Experiments are carried out to prove the performance of the proposed model over some existing methods. The obtained results confirm the efficiency of the method.展开更多
While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are n...While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing image.Active contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction technique.Firstly,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour model.As better energy functions are developed,the power assist of snakes becomes effective.New internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of interest.After normalization processing,energies are iterated using greedy algorithm to accelerate convergence rate.The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.展开更多
This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionba...This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionbased active contour techniques.In the first method,iterative threshold is performed for manual cropped preprocessed image,and active contour is applied thereafter.To overcome manual cropping in the second method,an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation,the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts,specifically by taking the difference in their mean values.Accordingly,the difference in the mean value of the third method is 1.0853,which indicates the closeness of the segmentation.Moreover,the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system,classification is done and results in a sensitivity of 94.73%,accuracy of 93.93%,and Mathew’s correlation coefficient(MCC)of 0.876.展开更多
In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the ...In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.展开更多
The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic reso...The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.9781 and 0.9567, for LV mass these values are 0.9249 and 0.8554. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.展开更多
In this paper we propose a new model for segmentation of an image under some geometrical constraints in order to detect special regions of interest.Our work is based on the recent work by Gout et al.[Numer.Algorithms,...In this paper we propose a new model for segmentation of an image under some geometrical constraints in order to detect special regions of interest.Our work is based on the recent work by Gout et al.[Numer.Algorithms,39(2005),pp.155-173 and 48(2008),pp.105-133]using geodesic active contours models,by combining it with the idea of a piecewise constant Mumford-Shah model as with the non-selective Chan-Vese segmentation.Numerical tests show that our method is more robust than the previous works.展开更多
Moving object segmentation is one of the most challenging issues in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mixture model (GMM) and ...Moving object segmentation is one of the most challenging issues in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Our algorithm integrates the GMM background model, shadow elimination term and curve evolution edge stopping term into energy function. It achieves more accurate segmentation than existing methods of the same type. Promising results on real images demonstrate the potential of the presented method.展开更多
The lamina cribrosa is affected by intraocular pressure, which is the major risk of glaucoma. However, the capability to evaluate the lamina cribrosa in vivo has been limited until recently due to poor image quality a...The lamina cribrosa is affected by intraocular pressure, which is the major risk of glaucoma. However, the capability to evaluate the lamina cribrosa in vivo has been limited until recently due to poor image quality and the posterior laminar displacement of glaucomatous eyes. In this study, we propose an automatic method to measure the anterior lamina cribrosa surface depth (ALCSD), including a method for detecting Bruch's membrane opening (BMO) based on k-means and region-based active contour. An anterior lamina cribrosa surface segmentation method based on energy constraint is also proposed. In BMO detection, we initialize the Chan-Vese active contour model by using the segmentation map of the k-means cluster. In the segmentation of anterior lamina cribrosa surface, we utilize the energy function in each A-scan to establish a set of candidates. The points in the set that fail to meet the constraints are removed. Finally, we use the B-spline fitting method to obtain the results. The proposed automatic method can model the posterior laminar displacement by measuring the ALCSD. This method achieves a mean error of 45.34 μm in BMO detection. The mean errors of the anterior lamina cribrosa surface are 94.1% within five pixels and 76.1% within three pixels.展开更多
A novel segmentation method for medical image with intensity inhomogeneity is introduced.In the proposed active contour model,both region and gradient information are taken into consideration.The former,i.e.,region-ba...A novel segmentation method for medical image with intensity inhomogeneity is introduced.In the proposed active contour model,both region and gradient information are taken into consideration.The former,i.e.,region-based fitting energy,draws upon the region information and guarantees the accurate extraction of inhomogeneous image's local information.The latter,i.e.,an edge indicator,weights the length penalizing term to consider the gradient constrain.Moreover,signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set function.Experiments for synthetic and real images demonstrate the feasibility and superiority of the proposed model.展开更多
In-situ layerwise imaging measurement of laser powder bed fusion(LPBF)provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity.Using high-resolution camera...In-situ layerwise imaging measurement of laser powder bed fusion(LPBF)provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity.Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers,which successfully monitored the contours of components and evaluated their accuracy.However,research for the methods of in-situ 3D contour measurement or component edge warping identification is rare.In this study,a 3D contour mea-surement method combining gray intensity and phase difference is proposed,and its accuracy is verified by designed experiments.The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function.This method can detect the deviations of common ge-ometric features as well as warpage at LPBF component edges,and provides fundamental data for in-situ quality monitoring tools.展开更多
One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and b...One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.展开更多
A new edge detection method combining the scanning window central edge (SWCE) detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pi...A new edge detection method combining the scanning window central edge (SWCE) detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pixel value means to perform an optimal edge detection, and then proposes an improved active contour model with modified energy functions to refine the location of the edges. The initial nodes of the improved active contour model are automatically found from the vectorised results of the SWCE detector. Tests on simulated speckled images and real airborne SAR images show that the combined method can benefit from the advantages of the both techniques and get satisfactory edge detection and localization abilities at the same time.展开更多
This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind o...This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind of image segmentation is still a challenging task.The proposed method uses both the region and boundary information to achieve accurate segmentation results.The region information can help to identify rough region of interest and prevent the boundary leakage problem.It makes use of normalized nonlocal comparisons between pairs of patches in each region,and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation.The boundary information can help to detect the precise location of the target object,it makes use of the geodesic active contour model to obtain the target boundary.The corresponding variational segmentation problem is implemented by a level set formulation.We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function.At last,experimental results on synthetic images and real images are shown in the paper with promising results.展开更多
This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour with...This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour withgradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images.Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction methodbased on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, thispaper introduces a weight factor to improve support vector machine, offering different classes of samples withdifferent weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine.Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.展开更多
In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution....In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution.Within this research,there is no exact template of the object;instead only several samples are given.The proposed method,called the parametric distribution prior model,extends our previous model by adding the training procedure to learn the prior distribution of the objects.Then this paper establishes the energy function of the active contour model(ACM)with consideration of this parametric form of prior distribution.Therefore,during the process of segmenting,the template can update itself while the contour evolves.Experiments are performed on the airplane data set.Experimental results demonstrate the potential of the proposed method that with the information of prior distribution,the segmentation effect and speed can be both improved efficaciously.展开更多
基金Supported by the National Basic Research Program of China(2011CB707904)the Natural Science Foundation of China(61472289)Hubei Province Natural Science Foundation of China(2015CFB254)
文摘Segmenting the lesion regions from the ultrasound (US) images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound (HIFU), as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons: (1) the blurry or missing boundaries of lesion regions in the HIFU images and (2) the deformation of uterine fibroids caused by the patient's breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier (ALM). Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.
基金supported by the National Natural Science Foundation of China(4117132741301361)+2 种基金the National Key Basic Research Program of China(973 Program)(2012CB719903)the Science and Technology Project of Ministry of Transport of People’s Republic of China(2012-364-X11-803)the Shanghai Municipal Natural Science Foundation(12ZR1433200)
文摘Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar(SAR) images. A novel method is proposed based on integrating the geometric active contour(GAC) and the support vector machine(SVM)models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items: the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.
文摘A fast two-stage geometric active contour algorithm for image segmentation is developed. First, the Eikonal equation problem is quickly solved using an improved fast sweeping method, and a criterion of local minimum of area gradient (LMAG) is presented to extract the optimal arrival time. Then, the final time function is passed as an initial state to an area and length minimizing flow model, which adjusts the interface more accurately and prevents it from leaking. For object with complete and salient edge, using the first stage only is able to obtain an ideal result, and this results in a time complexity of O(M), where M is the number of points in each coordinate direction. Both stages are needed for convoluted shapes, but the computation cost can be drastically reduced. Efficiency of the algorithm is verified in segmentation experiments of real images with different feature.
基金Supported by the National Natural Science Foundation of China(60805004)the State Key Lab of Space Medicine Fundamen-tals and Application(SMFA09A16)
文摘Gradient vector flow (GVF) is an effective external force for active contours, but its iso- tropic nature handicaps its performance. The recently proposed gradient vector flow in the normal direction (NGVF) is anisotropic since it only keeps the diffusion along the normal direction of the isophotes; however, it has difficulties forcing a snake into long, thin boundary indentations. In this paper, a novel external force for active contours called normally generalized gradient vector flow (NGGVF) is proposed, which generalizes the NGVF formulation to include two spatially varying weighting functions. Consequently, the proposed NGGVF snake is anisotropic and would improve ac- tive contour convergence into long, thin boundary indentations while maintaining other desirable properties of the NGVF snake, such as enlarged capture range, initialization insensitivity and good convergence at concavities. The advantages on synthetic and real images are demonstrated.
基金supported by the Science Council,Taiwan,under Grant No.NSC95-2221-E-006-475-MY2
文摘In the present study, a generalized active contour model of gradient vector flow is combined with the video techniques of Argus system to delineate and track sequential nearshore wave crest profiles in the shoaling process, up to their breaking on the shoreline. Previous applications of active contour models to water wave problems are limited to controllable wave tank experiments. By contrast, our application in this study is in a nearshore field environment where oblique images obtained under natural and varying condition of ambient light are employed. Existing Argus techniques produce plane image data or time series data from a selected small subset of discrete pixels. By contrast, the active contour model produces line image data along continuous visible curves such as wave crest profiles. The combination of these two existing techniques, the active contour model and Argus methodologies, facilitates the estimates of the direction wave field and phase speeds within the whole area covered by camera. These estimates are useful for the purpose of inverse calculation of the water depth. Applications of the present techniques to Hsi-tzu bay where a beach restoration program is currently undertaken are illustrated. This extension of Argus video techniques provides new application of optical remote sensing to study the hydrodynamics and morphology of a nearshore environment.
文摘A novel active contour model is proposed, which incorporates local information distributions in a fuzzy energy function to effectively deal with the intensity inhomogeneity. Moreover, the proposed model is convex with respect to the variable which is used for extracting the contour. This makes the model independent on the initial condition and suitable for an automatic segmentation. Furthermore, the energy function is minimized in a computationally efficient way by calculating the fuzzy energy alterations directly. Experiments are carried out to prove the performance of the proposed model over some existing methods. The obtained results confirm the efficiency of the method.
基金Sponsoreds by the National Natural Science Foundation of China (Grant No. 60575016)
文摘While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing image.Active contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction technique.Firstly,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour model.As better energy functions are developed,the power assist of snakes becomes effective.New internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of interest.After normalization processing,energies are iterated using greedy algorithm to accelerate convergence rate.The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.
文摘This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionbased active contour techniques.In the first method,iterative threshold is performed for manual cropped preprocessed image,and active contour is applied thereafter.To overcome manual cropping in the second method,an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation,the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts,specifically by taking the difference in their mean values.Accordingly,the difference in the mean value of the third method is 1.0853,which indicates the closeness of the segmentation.Moreover,the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system,classification is done and results in a sensitivity of 94.73%,accuracy of 93.93%,and Mathew’s correlation coefficient(MCC)of 0.876.
基金Supported by the State Key Program of National Natural Science of China (No. 61003134, 60736008)the National Natural Science Foundation of China (No. 60803082)the Key Program of Natural Science of Beijing (No.4081002)
文摘In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.
基金supported by Department of Science and Technology, Ministry of Science and Technology, India (No. DST/TSG/ICT/2010/08)
文摘The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.9781 and 0.9567, for LV mass these values are 0.9249 and 0.8554. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.
文摘In this paper we propose a new model for segmentation of an image under some geometrical constraints in order to detect special regions of interest.Our work is based on the recent work by Gout et al.[Numer.Algorithms,39(2005),pp.155-173 and 48(2008),pp.105-133]using geodesic active contours models,by combining it with the idea of a piecewise constant Mumford-Shah model as with the non-selective Chan-Vese segmentation.Numerical tests show that our method is more robust than the previous works.
基金Supported by National Basic Research Program of China (Grant No.2006CB303105)the Chinese Ministry of Education Innovation Team Fund Project (Grant No.IRT0707)+3 种基金the National Natural Science Foundation of China (Grant Nos.60673109 and 60801053)Beijing Excellent Doctoral Thesis Program (Grant No. YB20081000401)Beijing Municipal Natural Science Foundation (Grant No.4082025)Doctoral Foundation of China (Grant No.20070004037)
文摘Moving object segmentation is one of the most challenging issues in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Our algorithm integrates the GMM background model, shadow elimination term and curve evolution edge stopping term into energy function. It achieves more accurate segmentation than existing methods of the same type. Promising results on real images demonstrate the potential of the presented method.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61672542 and 61573380.
文摘The lamina cribrosa is affected by intraocular pressure, which is the major risk of glaucoma. However, the capability to evaluate the lamina cribrosa in vivo has been limited until recently due to poor image quality and the posterior laminar displacement of glaucomatous eyes. In this study, we propose an automatic method to measure the anterior lamina cribrosa surface depth (ALCSD), including a method for detecting Bruch's membrane opening (BMO) based on k-means and region-based active contour. An anterior lamina cribrosa surface segmentation method based on energy constraint is also proposed. In BMO detection, we initialize the Chan-Vese active contour model by using the segmentation map of the k-means cluster. In the segmentation of anterior lamina cribrosa surface, we utilize the energy function in each A-scan to establish a set of candidates. The points in the set that fail to meet the constraints are removed. Finally, we use the B-spline fitting method to obtain the results. The proposed automatic method can model the posterior laminar displacement by measuring the ALCSD. This method achieves a mean error of 45.34 μm in BMO detection. The mean errors of the anterior lamina cribrosa surface are 94.1% within five pixels and 76.1% within three pixels.
基金the National Natural Science Foundation of China (Nos.30801302 and 30872906)the "Medical and Engineering Crossing" Foundation of Shanghai Jiaotong University (No.40YG2009ZD102)
文摘A novel segmentation method for medical image with intensity inhomogeneity is introduced.In the proposed active contour model,both region and gradient information are taken into consideration.The former,i.e.,region-based fitting energy,draws upon the region information and guarantees the accurate extraction of inhomogeneous image's local information.The latter,i.e.,an edge indicator,weights the length penalizing term to consider the gradient constrain.Moreover,signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set function.Experiments for synthetic and real images demonstrate the feasibility and superiority of the proposed model.
基金This work was supported by the foundation of Key Research and Development Program of Hubei Province(2020BAB137)Shen-zhen Fundamental Research Program(JCYJ20210324142007022).
文摘In-situ layerwise imaging measurement of laser powder bed fusion(LPBF)provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity.Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers,which successfully monitored the contours of components and evaluated their accuracy.However,research for the methods of in-situ 3D contour measurement or component edge warping identification is rare.In this study,a 3D contour mea-surement method combining gray intensity and phase difference is proposed,and its accuracy is verified by designed experiments.The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function.This method can detect the deviations of common ge-ometric features as well as warpage at LPBF component edges,and provides fundamental data for in-situ quality monitoring tools.
文摘One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.
文摘A new edge detection method combining the scanning window central edge (SWCE) detector and an improved active contour model is proposed. The method first emploies the SWCE detector based on the difference of area pixel value means to perform an optimal edge detection, and then proposes an improved active contour model with modified energy functions to refine the location of the edges. The initial nodes of the improved active contour model are automatically found from the vectorised results of the SWCE detector. Tests on simulated speckled images and real airborne SAR images show that the combined method can benefit from the advantages of the both techniques and get satisfactory edge detection and localization abilities at the same time.
基金supported in part by the National Natural Science Foundation of China(11626214,11571309)the General Research Project of Zhejiang Provincial Department of Education(Y201635378)+3 种基金the Zhejiang Provincial Natural Science Foundation of China(LY17F020011)J.Peng is supported by the National Natural Science Foundation of China(11771160)the Research Promotion Program of Huaqiao University(ZQN-PY411)Natural Science Foundation of Fujian Province(2015J01254)
文摘This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind of image segmentation is still a challenging task.The proposed method uses both the region and boundary information to achieve accurate segmentation results.The region information can help to identify rough region of interest and prevent the boundary leakage problem.It makes use of normalized nonlocal comparisons between pairs of patches in each region,and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation.The boundary information can help to detect the precise location of the target object,it makes use of the geodesic active contour model to obtain the target boundary.The corresponding variational segmentation problem is implemented by a level set formulation.We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function.At last,experimental results on synthetic images and real images are shown in the paper with promising results.
基金This work was supported in part by National Natural Science Foundation of China under Grant Nos.61572063 and 61401308Natural Science Foundation of Hebei Province under Grant Nos.F2016201142,F2018210148,F2019201151 and F2020201025+3 种基金Science Research Project of Hebei Province under Grant Nos.BJ2020030,QN2016085 and QN2017306Foundation of President of Hebei University under Grant No.XZJJ201909Opening Foundation of Machine Vision Technology Innovation Center of Hebei Province under Grant Nos.2018HBMV01 and 2018HBMV02Natural Science Foundation of Hebei University under Grant Nos.2014-303 and 8012605.
文摘This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs)and discriminate them as benign or malignant. The system firstly uses variational level set active contour withgradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images.Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction methodbased on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, thispaper introduces a weight factor to improve support vector machine, offering different classes of samples withdifferent weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine.Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.
基金supported by the National Key R&D Program of China(2018YFC0309400)the National Natural Science Foundation of China(61871188)
文摘In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution.Within this research,there is no exact template of the object;instead only several samples are given.The proposed method,called the parametric distribution prior model,extends our previous model by adding the training procedure to learn the prior distribution of the objects.Then this paper establishes the energy function of the active contour model(ACM)with consideration of this parametric form of prior distribution.Therefore,during the process of segmenting,the template can update itself while the contour evolves.Experiments are performed on the airplane data set.Experimental results demonstrate the potential of the proposed method that with the information of prior distribution,the segmentation effect and speed can be both improved efficaciously.