Graph Cut Model Research Articles

Wetland Scene Segmentation of Remote Sensing Images Based on Lie Group Feature and Graph Cut Model

Wetland Scene Segmentation of Remote Sensing Images Based on Lie Group Feature and Graph Cut Model

Centerless Clustering.

Although lots of clustering models have been proposed recently, k-means and the family of spectral clustering methods are both still drawing a lot of attention due to their simplicity and efficacy. We first reviewed the unified framework of k-means and graph cut models, and then proposed a clustering method called k-sums where a k-nearest neighbor ( k-NN) graph is adopted. The main idea of k-sums is to minimize directly the sum of the distances between points in the same cluster. To deal with the situation where the graph is unavailable, we proposed k-sums-x that takes features as input. The computational and memory overhead of k-sums are both O(nk), indicating that it can scale linearly w.r.t. the number of objects to group. Moreover, the costs of computational and memory are Irrelevant to the product of the number of points and clusters. The computational and memory complexity of k-sums-x are both linear w.r.t. the number of points. To validate the advantage of k-sums and k-sums-x on facial datasets, extensive experiments have been conducted on 10 synthetic datasets and 17 benchmark datasets. While having a low time complexity, the performance of k-sums is comparable with several state-of-the-art clustering methods.

A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach, the Linear Iterative Clustering process is employed to cluster the potential features of the preprocessed image, which is then combined with GrabCut to prevent the issues that arise when the number of superpixels is minimized. In addition, the proposed HLC-BC-GCST scheme benefits of the advantages of the Gaussian mixture model (GMM) on the extracted features from the iterative clustering method, based on which the mapping is performed to describe the energy function. Then, Bayes classification is used for reconstructing the graph cut model from the extracted energy function derived from the GMM model-based Linear Iterative Clustering features for better computation and implementation. Finally, the boundary optimization method is utilized to considerably minimize the roughness of cervical cells, which contains the cytoplasm and nuclei regions, using the GrabCut algorithm to facilitate improved segmentation accuracy. The results of the proposed HLC-BC-GCST scheme are 6% better than the results obtained by other standard detection approaches of cervical cancer using graph cuts.

Water Extraction in SAR Images Using Features Analysis and Dual-Threshold Graph Cut Model

Timely identifying and detecting water bodies from SAR images are significant for flood monitoring and water resources management. In recent decades, deep learning has been applied to water extraction but is subject to the large difficulty of acquiring SAR dataset of various water bodies types, as well as heavy labeling work. In addition, the traditional methods mostly occur over the large, open lakes and rivers, rarely focusing on complex areas such as the urban water, and cannot automatically acquire the classification threshold. To address these issues, a novel water extraction method is proposed with high accuracy in this paper. Firstly, a multiscale feature extraction using a Gabor filter is conducted to reduce the noise and roughly identify water feature. Secondly, we apply the Otsu algorithm as well as a voting strategy to initially extract the homogeneous regions and for subsequent Gaussian mixture model (GMM). Finally, the dual threshold is obtained from the fitted Gaussian distribution of water and non-water, which is integrated into the graph cut model to redefine the weights of the edges, then constructing the energy function of the water map. The dual-threshold graph cut (DTGC) model precisely pinpoints the water location by minimizing the energy function. To verify the efficiency and robustness, our method and comparison methods, including the IGC method and IACM method, are tested on six different types of water bodies, by performing the accuracy assessment via comparing outcomes with the manually labeled ground truth. The qualitative and quantitative results show that the overall accuracy of our method for the whole dataset all surpasses 99%, along with an obvious improvement of the Kappa, F1-score, and IoU indicators. Therefore, DTGC method has the absolute advantage of automatically capturing water maps in different scenes of SAR images without specific prior knowledge and can also determine the optimal threshold range.

Semi-automatic liver segmentation based on probabilistic models and anatomical constraints

Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.

Iterative Min Cut Clustering Based on Graph Cuts.

Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time.

Iterated shape-bias graph cut with application to ellipse segmentation

We present a novel graph cut method for iterated segmentation of objects with specific shape bias (SBGC). In contrast with conventional graph cut models which emphasize the regional appearance only, the proposed SBGC takes the shape preference of the interested object into account to drive the segmentation. Therefore, the SBGC can ensure a more accurate convergence to the interested object even in complicated conditions where the appearance cues are inadequate for object/background discrimination. In particular, we firstly evaluate the segmentation by simultaneously considering its global shape and local edge consistencies with the object shape priors. Then these two cues are formulated into a graph cut framework to seek the optimal segmentation that maximizing both of the global and local measurements. By iteratively implementing the optimization, the proposed SBGC can achieve joint estimation of the optimal segmentation and the most likely object shape encoded by the shape priors, and eventually converge to the candidate result with maximum consistency between these two estimations. Finally, we take the ellipse shape objects with various segmentation challenges as examples for evaluation. Competitive results compared with state-of-the-art methods validate the effectiveness of the technique.

Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image

Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image

Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image.

Ultrasound (US) imaging has the technical advantages for the functional evaluation of myocardium compared with other imaging modalities. However, it is a challenge of extracting the myocardial tissues from the background due to low quality of US imaging. To better extract the myocardial tissues, this study proposes a semi-supervised segmentation method of fast Superpixels and Neighborhood Patches based Continuous Min-Cut (fSP-CMC). The US image is represented by a graph, which is constructed depending on the features of superpixels and neighborhood patches. A novel similarity measure is defined to capture and enhance the features correlation using Pearson correlation coefficient and Pearson distance. Interactive labels provided by user play a subsidiary role in the semi-supervised segmentation. The continuous graph cut model is solved via a fast minimization algorithm based on augmented Lagrangian and operator splitting. Additionally, Non-Uniform Rational B-Spline (NURBS) curve fitting is used as post-processing to solve the low resolution problem caused by the graph-based method. 200 B-mode US images of left ventricle of the rats were collected in this study. The myocardial tissues were segmented using the proposed fSP-CMC method compared with the method of fast Neighborhood Patches based Continuous Min-Cut (fP-CMC). The results show that the fSP-CMC segmented the myocardial tissues with a higher agreement with the ground truth (GT) provided by medical experts. The mean absolute distance (MAD) and Hausdorff distance (HD) were significantly lower than those values of fP-CMC (p < 0.05), while the Dice was significantly higher (p < 0.05). In conclusion, the proposed fSP-CMC method accurately and effectively segments the myocardiumn in US images. This method has potentials to be a reliable segmentation method and useful for the functional evaluation of myocardium in the future study.

Video object extraction using optimized smoothed dirichlet process multi-view learning with improved adaptive modified Markova random field

Video object extraction (VOE) using segmentation from a video sequence is a very important task in editing and multimedia analysis for film making. Most of the VOE approaches required prior knowledge about background and foreground to extract target objects. In this paper, an Optimized smoothed Dirichlet Process Multi-view learning with improved adaptive Modified Markov Random Field which is enhanced by adaptive shape prior modified graph cut (OsDPMVL-IASMMRF) model has been extended for video-based object extraction. The contour tracking has been additionally included OsDPMVL-IASMMRF for VOE. The Teh–Chin algorithm has been used with OsDPMVL-IASMMRF for predicting the contour in the current frame by matching the extracted object contour from the previous segmented frame. The contour tracking propagates the shape of the target object, whereas the OsDPMVL-IASMMRF segmentation refined the object boundary and the shape for enhancing the accuracy of video segmentation. The experimental outcomes show that the proposed approach provides better segmentation results in terms of accuracy, precision and recall.

Wire segmentation for printed circuit board using deep convolutional neural network and graph cut model

Printed circuit board wire segmentation based on computed tomography (CT) image can help subsequently locate and estimate inner faults of circuit in an automatic and non-destructive manner. However, CT imaging is prone to suffer from scattered artefacts, metal artefacts and other interference, destroying compact boundary structures of wires. Wires have the characteristic of dense local distribution, and massive vias, pads, and coppers can appear close to wires, resulting in mazy recognition surroundings. The above-mentioned problems bring great difficulty for high-accuracy recognition and location of wire segmentation. In this study, considering that deep convolutional neural network (DCNN) with powerful feature representation can recognise wires in confused surroundings, and graph cut (GC) model relying on grayscale and local texture information specialises in protecting edge structures of wires, the authors propose an effective framework called DCNN-GC that employs DCNN to obtain global semantic prior to guide the GC model to accomplish satisfactory wire segmentation. The authors qualitative and quantitative results demonstrate outstanding performance, and achieve overwhelming intersection over union compared with traditional and DCNN-based methods.

Interactive Image Segmentation using Improved Adaptive Markov Random Field Approach

Background/Objectives: To interactively split an object of interest from the remaining image with better smoothing by introducing improved adaptive Markov Random field resolving the problem of much noise. Methods/Statistical analysis: By utilizing a Dirichlet process multiple - view learning scheme, the unlabelled pixel labels are calculated by using the seed pixels. It is used for supporting the multiple-view learning in order to incorporate the constraints of both appearance and boundary constraint, and the Dirichlet process mixture-based nonlinear classification for concurrently modelling the image features and distinguishing the differences between the classes of object and background. The MRF field is utilized for providing the smoothness in segment labels. Findings: In Markov Random Field (MRF) based scheme, only the pixel and the surrounding pixels relationships are considered. The microscopic image processing result along with low noise is bad. To solve this problem, the adaptive MRF method based on region; it exploits Graph Cuts for inference. The different type of images produces the different pre-segmentation results. The connection degree between current and its linked regional blocks is denoted by connection parameter. If the connection parameter value is high, large connection degree between regional block and neighbouring regional blocks is defined. Otherwise lower connection degree between regional block and neighbourhood regional blocks. The adaptive MRF smoothing is more accurate and efficient segmentation result than the traditional MRF based Smoothing method. However, the appropriate parameter selection is a difficult task for practical image segmentation which can be solved by introducing an improved adaptive MRF model by using a modified graph cutter. Thus, the image segmentation is achieved based on modified graph-cut model using a novel energy function without the regularizing parameter. Improvements/Applications: The improved adaptive Markov Random Field approach interactively segment images with better smoothness than most of the current approaches.

A Practical GrabCut Color Image Segmentation Based on Bayes Classification and Simple Linear Iterative Clustering

In order to solve the segmentation degradation phenomenon when the number of super pixels is low, we propose a novel color image segmentation algorithm based on GrabCut. The method integrates Bayes classification with simple linear iterative clustering (SLIC) and then use the GrabCut method to obtain the segmentation. The SLIC is applied to cluster the features of a color image and integrated it into the GrabCut framework to overcome the problem of the image segmentation deterioration when the number of super pixels is low. In addition, we extend the Gaussian mixture model (GMM) to SLIC features and GMM based on SLIC is constructed to describe the energy function. The color clustering can be suitably integrated into the GrabCut framework and fused with the color feature to achieve more superior image segmentation performance than the original GrabCut method. For easier implementation and more efficient computation, the Bayes classification is chosen for reconstruction of the simplified graph cut model instead of the original graph cut based on the SLIC model. The min-cut algorithm technique served as the division measure in the simplified image space for more discriminating power. A classification strategy is presented, to effectively adjust the energy function so that the Bayes classification and SLIC features are efficiently integrated to achieve more robust segmentation performance. Finally, boundary optimization is proposed to dramatically reduce the boundary roughness of the GrabCut algorithm with satisfactory segmentation accuracy. As a practical application, the superior performance of our proposed method was demonstrated through a large number of comparative tests.

Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model

Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions obtained by the spectral-spatial segmentation process is presented. Our method includes the following steps. First, the probabilistic support vector machines (SVM) classifier is used to estimate probabilities belonging to each information class. Second, an extended JBF is employed to perform image smoothing on the probability maps. By using our JBF process, salt-and-pepper classification noise in homogeneous regions can be effectively smoothed out while object boundaries in the original image are better preserved as well. Third, a sequence of modified bi-labeling graph cut models is constructed for each information class to extract the desirable object belonging to the corresponding class from the smoothed probability maps. Finally, a classification map is achieved by merging the segmentation maps obtained in the last step using a simple and effective rule. Experimental results based on three benchmark airborne hyperspectral datasets with different resolutions and contexts demonstrate that our method can achieve 8.56%–13.68% higher overall accuracies than the pixel-wise SVM classifier. The performance of our method was further compared to several classical hyperspectral image classification methods using objective quantitative measures and a visual qualitative evaluation.

Robust natural image segmentation by using spatially constrained multivariate mixed Student’s t-distribution and TV flow edge

In this paper, a new method of spatially constrained energy function and total variation flow edge (TVFE) model is proposed for natural image segmentation. As the segmentation process of natural image is sensitive to corrupted noise, the Markov random field (MRF) based spatially constrained information is combined with Graph cut model. Secondly, a total variation flow edge is extracted by calculating the accumulated gradient difference from a nonlinear diffusion filter image. Thirdly, to improve the segmentation performance, the spatially constrained region term and the TVFE edge term are combined together. As the optimization solution of the proposed model is NP hard problem, and then an Expectation Maximum and Multilayer graph cut (EM2GC) algorithm is presented. Lastly, testing experiments are carried out on some synthetic images and real natural scene images, which demonstrate the superiority of our proposed method, such as the effectiveness, robustness, and high accuracy.

Superpixel-Based Segmentation for 3D Prostate MR Images.

This paper proposes a method for segmenting the prostate on magnetic resonance (MR) images. A superpixel-based 3D graph cut algorithm is proposed to obtain the prostate surface. Instead of pixels, superpixels are considered as the basic processing units to construct a 3D superpixel-based graph. The superpixels are labeled as the prostate or background by minimizing an energy function using graph cut based on the 3D superpixel-based graph. To construct the energy function, we proposed a superpixel-based shape data term, an appearance data term, and two superpixel-based smoothness terms. The proposed superpixel-based terms provide the effectiveness and robustness for the segmentation of the prostate. The segmentation result of graph cuts is used as an initialization of a 3D active contour model to overcome the drawback of the graph cut. The result of 3D active contour model is then used to update the shape model and appearance model of the graph cut. Iterations of the 3D graph cut and 3D active contour model have the ability to jump out of local minima and obtain a smooth prostate surface. On our 43 MR volumes, the proposed method yields a mean Dice ratio of 89.3 ±1.9%. On PROMISE12 test data set, our method was ranked at the second place; the mean Dice ratio and standard deviation is 87.0±3.2%. The experimental results show that the proposed method outperforms several state-of-the-art prostate MRI segmentation methods.

Image segmentation based on weighting boundary information via graph cut

The graph cut model has been widely used in image segmentation, in which both the region and boundary information play important roles for accurate segmentation. However, how to effectively model and combine these two information is still a challenge. In this paper, we improve the conventional graph cut methods by combining the region and boundary information with an effective and straightforward way. When modeling the region information, the component-wise expectation–maximization for Gaussian mixtures algorithm is used to learn the parameters of the prior knowledge. When modeling the boundary information, the weighting patch is used to represent the similarities of the neighboring pixels. Then the region and boundary information are combined by a weighting parameter, where the weight is small for boundary pixels and is large for non-boundary pixels. Finally, experiments on various images from the Berkeley and MSRC data sets were conducted to demonstrate the effectiveness of the proposed method.

Automatic segmentation of lymph vessel wall using optimal surface graph cut and hidden Markov Models.

We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determine edge based filters for both of these segmentations, and use these features to build Gaussian probability distributions for both the vessel walls and the background. From this we infer the emission probability for the HMM, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into one of cost minimization, with each node in the graph corresponding to one state, and the weight for each node being defined using its emission probability. We define the inter-relations between neighboring nodes using the transmission probability. Having constructed the problem, it is solved using the Viterbi algorithm, allowing the vessel to be reconstructed. The optimal solution can be found in polynomial time. We present qualitative and quantitative analysis to show the performance of the proposed method.

Combining pixel-level and patch-level information for segmentation

The image patch has been widely used in many applications, such as image denoising and segmentation, in which the patch-level information is utilized by comparing the similarities between pairwise patches. Nevertheless, the similarities between patches are not accurate enough for irregular shaped objects, which may lead to over-smoothness for the details around image boundary regions. Most graph cut based methods introduce the patch-level information into the boundary term of the energy function, which, however, would dramatically increase the time complexity due to the similarity computations of all pairwise patches. To overcome above issues and effectively utilize the patch information in graph cut model, in this paper, we first utilize the Gaussian mixture model (GMM) to construct the structural features of patches. Therefore, the patch-level information can be effectively obtained based on the learning of GMM. Then we combine the pixel-level information and patch-level information together to further improve the segmentation accuracy for the details around boundary regions. Finally, the combination information is introduced into the region energy term of the graph cut framework. Experimental results on various images from the Berkeley and MSRC data sets demonstrate the effectiveness of the proposed method.

An Interactive Segmentation Method Based on Superpixel

This paper proposes an interactive image-segmentation method which is based on superpixel. To achieve fast segmentation, the method is used to establish a Graphcut model using superpixels as nodes, and a new energy function is proposed. Experimental results demonstrate that the authors' method has excellent per- formance in terms of segmentation accuracy and computation efficiency compared with other segmentation algo- rithm based on pixels.