The Experts below are selected from a list of 28422 Experts worldwide ranked by ideXlab platform
Olga Veksler - One of the best experts on this subject based on the ideXlab platform.
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star shape prior for Graph Cut image segmentation
European Conference on Computer Vision, 2008Co-Authors: Olga VekslerAbstract:In recent years, segmentation with Graph Cuts is increasingly used for a variety of applications, such as photo/video editing, medical image processing, etc. One of the most common applications of Graph Cut segmentation is extracting an object of interest from its background. If there is any knowledge about the object shape (i.e. a shape prior), incorporating this knowledge helps to achieve a more robust segmentation. In this paper, we show how to implement a star shape prior into Graph Cut segmentation. This is a generic shape prior, i.e. it is not specific to any particular object, but rather applies to a wide class of objects, in particular to convex objects. Our major assumption is that the center of the star shape is known, for example, it can be provided by the user. The star shape prior has an additional important benefit - it allows an inclusion of a term in the objective function which encourages a longer object boundary. This helps to alleviate the bias of a Graph Cut towards shorter segmentation boundaries. In fact, we show that in many cases, with this new term we can achieve an accurate object segmentation with only a single pixel, the center of the object, provided by the user, which is rarely possible with standard Graph Cut interactive segmentation.
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parameter selection for Graph Cut based image segmentation
British Machine Vision Conference, 2008Co-Authors: Bo Peng, Olga VekslerAbstract:The Graph Cut based approach has become very popular for interactive segmentation of the object of interest from the background. One of the most important and yet largely unsolved issues in the Graph Cut segmentation framework is parameter selection. Parameters are usually fixed beforehand by the developer of the algorithm. There is no single setting of parameters, however, that will result in the best possible segmentation for any general image. Usually each image has its own optimal set of parameters. If segmentation of an image is not as desired under the current setting of parameters, the user can always perform more interaction until the desired results are achieved. However, significant interaction may be required if parameter settings are far from optimal. In this paper, we develop an algorithm for automatic parameter selection. We design a measure of segmentation quality based on different features of segmentation that are combined using AdaBoost. Then we run the Graph Cut segmentation algorithm for different parameter values and choose the segmentation of highest quality according to our learnt measure. We develop a new way to normalize feature weights for the AdaBoost based classifier which is particularly suitable for our framework. Experimental results show a success rate of 95.6% for parameter selection.
Philip H S Torr - One of the best experts on this subject based on the ideXlab platform.
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measuring uncertainty in Graph Cut solutions efficiently computing min marginal energies using dynamic Graph Cuts
Lecture Notes in Computer Science, 2006Co-Authors: Pushmeet Kohli, Philip H S TorrAbstract:In recent years the use of Graph-Cuts has become quite popular in computer vision. However, researchers have repeatedly asked the question whether it might be possible to compute a measure of uncertainty associated with the Graph-Cut solutions. In this paper we answer this particular question by showing how the min-marginals associated with the label assignments in a MRF can be efficiently computed using a new algorithm based on dynamic Graph Cuts. We start by reporting the discovery of a novel relationship between the min-marginal energy corresponding to a latent variable label assignment, and the flow potentials of the node representing that variable in the Graph used in the energy minimization procedure. We then proceed to show how the min-marginal energy can be computed by minimizing a projection of the energy function defined by the MRF. We propose a fast and novel algorithm based on dynamic Graph Cuts to efficiently minimize these energy projections. The min-marginal energies obtained by our proposed algorithm are exact, as opposed to the ones obtained from other inference algorithms like loopy belief propagation and generalized belief propagation. We conclude by showing how min-marginals can be used to compute a confidence measure for label assignments in labelling problems such as image segmentation.
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interactive image segmentation using an adaptive gmmrf model
European Conference on Computer Vision, 2004Co-Authors: Andrew Blake, Carsten Rother, Matthew Brown, Patrick Perez, Philip H S TorrAbstract:The problem of interactive foreground/background segmentation in still images is of great practical importance in image editing. The state of the art in interactive segmentation is probably represented by the Graph Cut algorithm of Boykov and Jolly (ICCV 2001). Its underlying model uses both colour and contrast information, together with a strong prior for region coherence. Estimation is performed by solving a Graph Cut problem for which very efficient algorithms have recently been developed. However the model depends on parameters which must be set by hand and the aim of this work is for those constants to be learned from image data.
Long Quan - One of the best experts on this subject based on the ideXlab platform.
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A surface reconstruction method using global Graph Cut optimization
International Journal of Computer Vision, 2006Co-Authors: Sylvain Paris, François X. Sillion, Long QuanAbstract:Surface reconstruction from multiple calibrated images has been mainly approached using local methods, either as a continuous optimization problem driven by level sets, or by discrete volumetric methods such as space carving. We propose a direct surface reconstruction approach which starts from a continuous geometric functional that is minimized up to a discretization by a global Graph-Cut algorithm operating on a 3D embedded Graph. The method is related to the stereo disparity computation based on Graph-Cut formulation, but fundamentally different in two aspects. First, existing stereo disparity methods are only interested in obtaining layers of constant disparity, while we focus on high resolution surface geometry. Second, most of the existing Graph-Cut algorithms only reach approximate solutions, while we guarantee a global minimum. The whole procedure is consistently incorporated into a voxel representation that handles both occlusions and discontinuities. We demonstrate our algorithm on real sequences, yielding remarkably detailed surface geometry up to 1/10th of a pixel.
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A Surface Reconstruction Method Using Global Graph Cut Optimization
2004Co-Authors: Sylvain Paris, François X. Sillion, Long QuanAbstract:The surface reconstruction from multiple calibrated images has been mainly approached using local methods, either as a continuous optimization driven by level sets, or as a discrete volumetric method of space carving. We here propose a direct surface reconstruction approach. It starts from a continuous geometric functional that is then minimized up to a discretization by a global Graph-Cut algorithm operating on a 3D embedded Graph. The method is related to the stereo disparity computation based on Graph-Cut formulation, but fundamentally different in two aspects. First, the existing stereo disparity methods are only interested in obtaining layers of constant disparity, while we focus on a surface geometry of high resolution. Second, only approximate solutions are reached by most of the existing Graph-Cut algorithms, while we reach a global minimum. The whole procedure is consistently incorporated into a voxel representation that handles both occlusions and discontinuities. It is demonstrated on real sequences, yielding remarkably detailed surface geometry up to $1/10$th pixel.
Xinjian Chen - One of the best experts on this subject based on the ideXlab platform.
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random walk and Graph Cut for co segmentation of lung tumor on pet ct images
IEEE Transactions on Image Processing, 2015Co-Authors: Deihui Xiang, Bin Zhang, Lirong Wang, Ivica Kopriva, Xinjian ChenAbstract:Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomoGraphy (PET) images and low contrast in computed tomoGraphy (CT) images, segmentation of tumor in the PET and CT images is a challenging task. In this paper, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and Graph Cut method is integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for Graph Cut segmentation on the PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-Cut method. A Graph, including two sub-Graphs and a special link, is constructed, in which one sub-Graph is for the PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For the PET, a downhill cost and a 3D derivative cost are proposed. For the CT, a shape penalty cost is integrated into the energy function which helps to constrain the tumor region during the segmentation. We validate our algorithm on a data set which consists of 18 PET-CT images. The experimental results indicate that the proposed method is superior to the Graph Cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved Graph Cut method.
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automatic liver segmentation based on shape constraints and deformable Graph Cut in ct images
IEEE Transactions on Image Processing, 2015Co-Authors: Xinjian Chen, Fei Shi, Weifang Zhu, Jie Tian, Dehui XiangAbstract:Liver segmentation is still a challenging task in medical image processing area due to the complexity of the liver’s anatomy, low contrast with adjacent organs, and presence of pathologies. This investigation was used to develop and validate an automated method to segment livers in CT images. The proposed framework consists of three steps: 1) preprocessing; 2) initialization; and 3) segmentation. In the first step, a statistical shape model is constructed based on the principal component analysis and the input image is smoothed using curvature anisotropic diffusion filtering. In the second step, the mean shape model is moved using thresholding and Euclidean distance transformation to obtain a coarse position in a test image, and then the initial mesh is locally and iteratively deformed to the coarse boundary, which is constrained to stay close to a subspace of shapes describing the anatomical variability. Finally, in order to accurately detect the liver surface, deformable Graph Cut was proposed, which effectively integrates the properties and inter-relationship of the input images and initialized surface. The proposed method was evaluated on 50 CT scan images, which are publicly available in two databases Sliver07 and 3Dircadb. The experimental results showed that the proposed method was effective and accurate for detection of the liver surface.
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automated segmentation of intraretinal cystoid macular edema for retinal 3d oct images with macular hole
International Symposium on Biomedical Imaging, 2015Co-Authors: Li Zhang, Weifang Zhu, Fei Shi, Haoyu Chen, Xinjian ChenAbstract:An automated method is proposed to segment and quantify the volume of cystoid macular edema (CME) for the abnormal retina with macular hole (MH) in 3D OCT images. The proposed framework consists of three parts: (1) preprocessing, which includes denoising, intraretinal layers segmentation and flattening, MH and vessel silhouettes exclusion; (2) coarse segmentation, in which an AdaBoost classifier is used to get the seeds and constrained regions for Graph Cut; (3) fine segmentation, in which a Graph Cut algorithm is used to get the refine segmentation result. The proposed method was evaluated in 3D OCT images from 18 typical patients with CMEs and MH. The true positive volume fraction (TPVF), false positive volume fraction (FPVF) and accuracy rate (ACC) for CME volume segmentation are 84.6%, 1.7% and 99.7%, respectively.
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gc asm synergistic integration of Graph Cut and active shape model strategies for medical image segmentation
Computer Vision and Image Understanding, 2013Co-Authors: Xinjian Chen, Jayaram K Udupa, Abass Alavi, Drew A TorigianAbstract:Image segmentation methods may be classified into two categories: purely image based and model based. Each of these two classes has its own advantages and disadvantages. In this paper, we propose a novel synergistic combination of the image based Graph-Cut (GC) method with the model based ASM method to arrive at the GC-ASM method for medical image segmentation. A multi-object GC cost function is proposed which effectively integrates the ASM shape information into the GC framework. The proposed method consists of two phases: model building and segmentation. In the model building phase, the ASM model is built and the parameters of the GC are estimated. The segmentation phase consists of two main steps: initialization (recognition) and delineation. For initialization, an automatic method is proposed which estimates the pose (translation, orientation, and scale) of the model, and obtains a rough segmentation result which also provides the shape information for the GC method. For delineation, an iterative GC-ASM algorithm is proposed which performs finer delineation based on the initialization results. The proposed methods are implemented to operate on 2D images and evaluated on clinical chest CT, abdominal CT, and foot MRI data sets. The results show the following: (a) An overall delineation accuracy of TPVF>96%, FPVF<0.6% can be achieved via GC-ASM for different objects, modalities, and body regions. (b) GC-ASM improves over ASM in its accuracy and precision to search region. (c) GC-ASM requires far fewer landmarks (about 1/3 of ASM) than ASM. (d) GC-ASM achieves full automation in the segmentation step compared to GC which requires seed specification and improves on the accuracy of GC. (e) One disadvantage of GC-ASM is its increased computational expense owing to the iterative nature of the algorithm.
Yao Wang - One of the best experts on this subject based on the ideXlab platform.
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segmentation of 3d high frequency ultrasound images of human lymph nodes using Graph Cut with energy functional adapted to local intensity distribution
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Jenwei Kuo, Jonathan Mamou, Yao Wang, Emi Saegusabeecroft, Junji Machi, Ernest J FeleppaAbstract:Previous studies by our group have shown that three-dimensional high-frequency quantitative ultrasound methods have the potential to differentiate metastatic lymph nodes from cancer-free lymph nodes dissected from human cancer patients. To successfully perform these methods inside the lymph node parenchyma, an automatic segmentation method is highly desired to exclude the surrounding thin layer of fat from quantitative ultrasound processing and accurately correct for ultrasound attenuation. In high-frequency ultrasound images of lymph nodes, the intensity distribution of lymph node parenchyma and fat varies spatially because of acoustic attenuation and focusing effects. Thus, the intensity contrast between two object regions (e.g., lymph node parenchyma and fat) is also spatially varying. In our previous work, nested Graph Cut demonstrated its ability to simultaneously segment lymph node parenchyma, fat, and the outer phosphate-buffered saline bath even when some boundaries are lost because of acoustic attenuation and focusing effects. This paper describes a novel approach called Graph Cut with locally adaptive energy to further deal with spatially varying distributions of lymph node parenchyma and fat caused by inhomogeneous acoustic attenuation. The proposed method achieved Dice similarity coefficients of 0.937+-0.035 when compared to expert manual segmentation on a representative dataset consisting of 115 three-dimensional lymph node images obtained from colorectal cancer patients.
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Segmentation of 3-D High-Frequency Ultrasound Images of Human Lymph Nodes Using Graph Cut With Energy Functional Adapted to Local Intensity Distribution
IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, 2017Co-Authors: Jonathan Mamou, Yao Wang, Junji Machi, Emi Saegusa-beecroft, Ernest J FeleppaAbstract:Previous studies by our group have shown that 3-D high-frequency quantitative ultrasound (QUS) methods have the potential to differentiate metastatic lymph nodes (LNs) from cancer-free LNs dissected from human cancer patients. To successfully perform these methods inside the LN parenchyma (LNP), an automatic segmentation method is highly desired to exclude the surrounding thin layer of fat from QUS processing and accurately correct for ultrasound attenuation. In high-frequency ultrasound images of LNs, the intensity distribution of LNP and fat varies spatially because of acoustic attenuation and focusing effects. Thus, the intensity contrast between two object regions (e.g., LNP and fat) is also spatially varying. In our previous work, nested Graph Cut (GC) demonstrated its ability to simultaneously segment LNP, fat, and the outer phosphate-buffered saline bath even when some boundaries are lost because of acoustic attenuation and focusing effects. This paper describes a novel approach called GC with locally adaptive energy to further deal with spatially varying distributions of LNP and fat caused by inhomogeneous acoustic attenuation. The proposed method achieved Dice similarity coefficients of 0.937±0.035 when compared with expert manual segmentation on a representative data set consisting of 115 3-D LN images obtained from colorectal cancer patients.
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nested Graph Cut for automatic segmentation of high frequency ultrasound images of the mouse embryo
IEEE Transactions on Medical Imaging, 2016Co-Authors: Jenwei Kuo, Jonathan Mamou, Orlando Aristizabal, Xuan Zhao, Jeffrey A Ketterling, Yao WangAbstract:We propose a fully automatic segmentation method called nested Graph Cut to segment images (2D or 3D) that contain multiple objects with a nested structure. Compared to other Graph-Cut-based methods developed for multiple regions, our method can work well for nested objects without requiring manual selection of initial seeds, even if different objects have similar intensity distributions and some object boundaries are missing. Promising results were obtained for separating the brain ventricles, the head, and the uterus region in the mouse-embryo head images obtained using high-frequency ultrasound imaging. The proposed method achieved mean Dice similarity coefficients of $0.87 \pm 0.04$ and $0.89 \pm 0.06$ for segmenting BVs and the head, respectively, compared to manual segmentation results by experts on 40 3D images over five gestation stages.
