Region-based Level Set Method Research Articles

GreyWolfLSM: an accurate oil spill detection method based on level set method from synthetic aperture radar imagery

ABSTRACT Oil spill detection (OSD) in marine areas is an application of synthetic aperture radar (SAR) images to protect aquatic life. In this paper, a new oil spill detection algorithm based on level set method (LSM) is presented. Dark spot detection, feature extraction, and classification are the main steps in the proposed method. In the first step, a new dark spot detection method in SAR images is presented, which introduces a combination of multi-objective grey wolf optimization (MOGWO) and K-means clustering to find the best threshold level for image segmentation. This method overcomes the K-means clustering by choosing optimal number of clusters and centers. In the feature extraction step, a vector consisting of 45 arrays of calculated Legendre moments in the main, gradient and radon transform of the image is used. The oil-suspicious areas are also discriminated by the supported vector machine (SVM) classifier, and their boundaries are applied as an initial contour to a new hierarchical region-based level-set method (HRLSM). Experiments are performed on the images acquired by Envisat, UAVSAR, TerraSAR-X, and Sentinel1 satellites. The results showed the reliability and robustness of the proposed method with high accuracy, even for noisy images with heterogeneous and weak boundaries.

A weighted region-based level set method for image segmentation with intensity inhomogeneity.

Image segmentation is a fundamental task in image processing and is still a challenging problem when processing images with high noise, low resolution and intensity inhomogeneity. In this paper, a weighted region-based level set method, which is based on the techniques of local statistical theory, level set theory and curve evolution, is proposed. Specifically, a new weighted pressure force function (WPF) is first presented to flexibly drive the closed contour to shrink or expand outside and inside of the object. Second, a faster and smoother regularization term is added to ensure the stability of the curve evolution and that there is no need for initialization in curve evolution. Third, the WPF is integrated into the region-based level set framework to accelerate the speed of the curve evolution and improve the accuracy of image segmentation. Experimental results on medical and natural images demonstrate that the proposed segmentation model is more efficient and robust to noise than other state-of-the-art models.

A level set method based on domain transformation and bias correction for MRI brain tumor segmentation

BackgroundIntensity inhomogeneity is one of the common artifacts in image processing. This artifact makes image segmentation more challenging and adversely affects the performance of intensity-based image processing algorithms. New methodIn this paper, a novel region-based level set method is proposed for segmenting the images with intensity inhomogeneity with applications to brain tumor segmentation in magnetic resonance imaging (MRI) scans. For this purpose, the inhomogeneous regions are first modeled as Gaussian distributions with different means and variances, and then transferred into a new domain, where preserves the Gaussian intensity distribution of each region but with better separation. Moreover, our method can perform bias field correction. To this end, the bias field is represented by a linear combination of smooth base functions that enables better intensity inhomogeneity modeling. Therefore, level set fundamental formulation and bias field are modified in the proposed approach. ResultsTo assess the performance of the proposed method, different inhomogeneous images, including synthetic images as well as real brain magnetic resonance images from BraTS 2017 dataset are segmented. Being evaluated by Dice, Jaccard, Sensitivity, and Specificity metrics, the results show that the proposed method suppresses the side effect of the over-smoothing object boundary and it has good accuracy in the segmentation of images with extreme intensity non-uniformity. The mean values of these metrics in brain tumor segmentation are 0.86 ± 0.03, 0.77 ± 0.05, 0.94 ± 0.04, 0.99 ± 0.003, respectively. Comparison with existing method(s)Our method were compared with six state-of-the-art image segmentation methods: Chan-Vese (CV), Local Intensity Clustering (LIC), Local iNtensity Clustering (LINC), Global inhomogeneous intensity clustering (GINC), Multiplicative Intrinsic Component Optimization (MICO), and Local Statistical Active Contour Model (LSACM) models. We used qualitative and quantitative comparison methods for segmenting synthetic and real images. Experiments indicate that our proposed method is robust to noise and intensity non-uniformity and outperforms other state-of-the-art segmentation methods in terms of bias field correction, noise resistance, and segmentation accuracy. ConclusionsExperimental results show that the proposed model is capable of accurate segmentation and bias field estimation simultaneously. The proposed model suppresses the side effect of the over-smoothing object boundary. Moreover, our model has good accuracy in the segmentation of images with extreme intensity non-uniformity.

Polyp-Net: A Multimodel Fusion Network for Polyp Segmentation

Computer-aided diagnosis of disease primarily depends on proper vision-based measurement (VBM). The traditional approach followed for diagnosis of colorectal cancer includes a manual screening of colorectum via a colonoscope and resection of polyps for histopathological analysis to decide the grade of malignancy. This procedure is time-consuming and expensive, and removal of benign polyp for analysis signifies the inefficiency of the diagnosis system. These drawbacks inspired us to develop an automatic vision-based analysis method for preliminary in vivo malignancy analysis of the polyp region. In this work, we have proposed a fusion-based polyp segmentation network, namely, Polyp-Net. Recently, convolutional neural networks (CNNs) have shown immense success in the domain of medical image analysis as it can exploit in-depth significant features with high discrimination capabilities. Therefore, motivated by these insights, we have proposed an enriched version of CNN with a nascent pooling mechanism, namely dual-tree wavelet pooled CNN (DT-WpCNN). The resultant segmented mask contains some surplus high-intensity regions apart from the polyp region. These shortcomings are avoided using a new variation of the region-based level-set method, namely, the local gradient weighting-embedded level-set method (LGWe-LSM), which shows a significant reduction of false-positive rate. The pixel-level fusion of the two enhanced methods shows more potentiality in the segmentation of the polyp regions. Our proposed network is trained on CVC-colon DB and tested on CVC-clinic DB. It achieves a dice score of 0.839, volume-similarity of 0.863, precision of 0.836, recall of 0.811, F1-score of 0.823, F2-score of 0.815, and Hausdorff distance of 21.796 which outperforms the existing baseline CNN’s and recent state-of-the-art methods.

A survey of level set method for image segmentation with intensity inhomogeneity

Image segmentation is a fundamental task in computer vision and image processing. Due to the presence of high noise, low resolution and intensity inhomogeneity, it is still a difficult problem in the practical applications. Level set methods have been widely used in image processing and computer vision. During the past decades, many models based on level set methods have been proposed to solve image segmentation with intensity inhomogeneity. It is necessary to conduct a comprehensive review and comparison of these models. Specifically, level set methods can be categorized into two groups, including edge-based level set methods (EBLSMs) and region-based level set methods (RBLSMs). This paper reviews some of the recent advances in EBLSMs and RBLSMs for segmenting image with intensity inhomogeneity. Their advantages and disadvantages are discussed in an objective point of view, and their performance is compared on image segmentation with intensity inhomogeneity. Finally, this paper further explores and discusses some open questions in segmenting images with intensity inhomogeneity.

A Local Region-Based Level Set Method With Markov Random Field for Side-Scan Sonar Image Multi-Level Segmentation

Side-scan sonar is widely utilized in the field of underwater exploration. Accurate segmentation of the side-scan sonar images is an essential part of sonar image processing. To improve the accuracy of image segmentation and reduce the occupancy of computing resources, a new active contour model for image segmentation is proposed in this article. Firstly, we make the energy function of each pixel determined by itself and its neighbors. This article embeds the local texture neighborhood region system and define its structure to against the noise and object boundary pollution of the image. Further, we introduce a Bayesian framework that embeds the Markov Random Field model and local texture information to counteract the problem of intensity inhomogeneity, improve the accuracy of segmentation, and reduce computational cost. Finally, according to this model, we customize a new multi-class energy function which applied to the level set function. By minimizing this energy equation, we can divide the image into three classes: object, shadow, and seabed background. The experimental results show that this method has a good effect on the synthesized high-noise sonar images and real sonar images, and also have significantly increased the accurate recognition of underwater targets.

An Improved Approach for Image Segmentation and Three-Dimensional Reconstruction

Themain contribution of this paper consists of introducing a novelmodel of three-dimensional reconstruction from multiple two-dimensional images. Actually, our proposedmodel presents the followingmain advantages; first, we improve the original region fitting energy in the general region-based level set method by an anisotropic diffusion to evolve the contour. Second, we use the Dijkstra algorithm to improve and allow simple and free initializations. Third, we adopt the domain decomposition method to reduce the computational cost for high-resolution images. Finally, in order to prove the efficiency and accuracy of our proposed method, experiments were performed on synthetic and real images.

Extending contour level set model for multi-class image segmentation with Application to Breast Thermography Images

Extraction of target objects with the desired intensity value in multi-region images is a challenging task. This paper presents a new modification to the region-based level set method called Extending Contour Level Set (ECLS) model. In classical level set methods, the intensity of each pixel is compared with both average intensities inside and outside the contour; therefore, image domain is segmented into two regions with the most difference between average intensities. In multi-region images, there is no reason the selected region belongs to the desired one. In our approach, with the assumption that the initial contour is located inside the target object, outside-contour pixels are allocated to the inside-contour region if they have an intensity value close to the inside-contour average intensity. The ECLS model is compared with well-known level set methods such as the Chan-Vese model, the local binary fitting energy model, the geodesic level set model, the local statistical active contour model, and the local and global fitting image model. Simulation results indicate the remarkable advantages of the proposed method in terms of accuracy and computational complexity. To show the ability of the ECLS model in practice, a two-step approach is proposed to segment breast thermography images. In the first step, the ECLS model is exploited to produce a semi-final region. Then, in the second step, a new Controlled Chan-Vese (CCV) model is presented leading to accurate results. The CCV model controls the distance between the average intensities inside and outside the contour based on the image statistics. Comparison with other segmentation methods shows the superiority of the proposed approach. In addition, statistical measures are calculated to demonstrate the significant capabilities of the proposed method.

A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation

Image segmentation plays an important role in the computer vision . However, it is extremely challenging due to low resolution, high noise and blurry boundaries. Recently, region-based models have been widely used to segment such images. The existing models often utilized Gaussian filtering to filter images, which caused the loss of edge gradient information. Accordingly, in this paper, a novel local region model based on adaptive bilateral filter is presented for segmenting noisy images. Specifically, we firstly construct a range-based adaptive bilateral filter, in which an image can well be preserved edge structures as well as resisted noise. Secondly, we present a data-driven energy model, which utilizes local information of regions centered at each pixel of image to approximate intensities inside and outside of the circular contour. The estimation approach has improved the accuracy of noisy image segmentation. Thirdly, under the premise of keeping the image original shape, a regularization function is used to accelerate the convergence speed and smoothen the segmentation contour. Experimental results of both synthetic and real images demonstrate that the proposed model is more efficient and robust to noise than the state-of-art region-based models.

Hierarchical coastline detection in SAR images based on spectral‐textural features and global–local information

This study presents a novel approach to detect the coastline from single-polarisation synthetic aperture radar (SAR) images. The proposed method encompasses land/sea segmentation, coastline detection, and refinement. A novel spectral–textural segmentation framework (STSF) is proposed by using the spectral–textural features extracted from the input image patches. The STSF distinguishes various coastal/sea types and is robust to noise. Also, a hierarchical region-based level set method (LSM) is proposed to detect the coastline, accurately. The first LSM step applies global information for evolution. The LSM initialisation is performed using the obtained rough segmentation, which is very practical as the final LSM evolution depends on the initial value, particularly on complex SAR images. The global region-based LSM (GRB-LSM) step modifies the previous segmentation and approaches to the coastline. To improve accuracy, a local region-based LSM (LRB-LSM) is proposed. Therefore, in the second LSM step, the LRB-LSM applies to the results of GRB-LSM step. The LRB-LSM improves the accuracy of the detected coastline while ensuring its smoothness. To verify the performance of the proposed method, several high-resolution SAR images from different microwave bands and various coastal environments are used. The performance of the proposed method is confirmed by the given experiments.

Integration of Spectral Histogram and Level Set for Coastline Detection in SAR Images

This study proposed a novel algorithm for coastline detection in single-polarization synthetic aperture radar (SAR) images based on the local spectral histogram (LSH) and the level set method (LSM). The proposed algorithm includes two main steps. In the processing step, a rough land/sea segmentation is done by utilizing a texture-based segmentation using LSH. In the postprocessing step, the region-based LSM is used to refine the previous segmentation and extract the coastline accurately. The level set (LS) function is initialized using the results of LSH segmentation. This prevents the development of spurious contours, becoming trapped in the local minimum, eliminates the manual support, and also speeds up the LS evolution. A hierarchical LS regularization is proposed using two Gaussian kernels, which is compatible with noisy images and able to detect very narrow regions. The proposed algorithm is able to detect a coastline at the full resolution of the input SAR image and is also robust to noise. A criterion to quantify the accuracy of the results was also proposed. The experimental results for a number of real high-resolution single-polarization SAR images demonstrate that the proposed method is robust to noise and efficient for the coastline detection in different coastal and sea environments.

A level set method for image segmentation based on Bregman divergence and multi-scale local binary fitting

Image segmentation is an important processing in many applications such as image retrieval and computer vision. The level set method based on local information is one of the most successful models for image segmentation. However, in practice, these models are at risk for existence of local minima in the active contour energy and the considerable computing-consuming. In this paper, a novel region-based level set method based on Bregman divergence and multi-scale local binary fitting(MLBF), called Bregman-MLBF, is proposed. Bregman-MLBF utilizes both global and local information to formulate a new energy function. The global information by Bregman divergence which can be approximated by the data-dependent weighted L2 − norm, not only accelerates the contour evolution, especially, when the contour is far away from object boundaries but also boosts the robustness to the initial placement. The local information is used to improve the capability of coping with intensity inhomogeneity and to attract the contour to stop at the object boundaries. The experiments conducted on synthetic images, real images and benchmark image datasets have demonstrated that Bregman-MLBF outperforms the piece-wise constant (PC) model in handling intensity inhomogeneity and is more effective than the local binary fitting model and more robust than the local and global intensity fitting model.

Local region-based level set approach for fast synthetic aperture radar image segmentation

Synthetic aperture radar (SAR) image segmentation is the key to SAR image automatic interpretation. However, speckle noise, intensity inhomogeneity, and irregular shaped objects with changing edge often make the SAR image segmentation very difficult, and existing algorithms have high computational complexity. We propose a region-based level set method using the local image intensity information. To represent the statistical characteristics of speckle noise, we first use a gamma statistical distribution to model every segmented SAR image. We then apply a modified region mean estimation formula to efficiently segment SAR images with inhomogeneity. Finally, Gaussian filtering is employed to regularize the level set function, which can avoid reinitialization. The experimental results on synthetic and real-world SAR images demonstrate that the proposed method has less computation cost, faster convergence rate, and more accurate segmentation results.

Simultaneous segmentation and bias field estimation using local fitted images

Level set methods often suffer from boundary leakage and inadequate segmentation when used to segment images with inhomogeneous intensities. To handle this issue, a novel region-based level set method was developed, in which two different local fitted images are used to construct a hybrid region intensity fitting energy functional. This novel method enables simultaneous segmentation of the regions of interest and estimation of the bias fields from inhomogeneous images. Our experiments on both synthetic images and a publicly available dataset demonstrate the feasibility and reliability of the proposed method.

A Coastline Detection Method in Polarimetric SAR Images Mixing the Region-Based and Edge-Based Active Contour Models

This paper proposes a coastline detection method for polarimetric synthetic aperture radar (SAR) images based on region-based and edge-based active contour models. It can be used to detect coastline accurately and fast. In this method, the region-based and edge-based active contour models are effectively combined by an important property of the likelihood ratio edge detector in polarimetric SAR images, which is proved by theory. Using low-resolution image obtained by multilook processing, we detect accurate and continued coarse coastlines by a region-based level set method. The property of the likelihood ratio edge detector of polarimetric SAR images along the coastline region is then analyzed. The coarse detection result is finally refined using a fast snake active contour model based on the edge property. Polarimetric SAR data acquired by RADARSAT-2 over a Singapore region and TerraSAR-X over a Berkeley region are both used to test the proposed algorithm. The experimental results show that the coastline is fast and accurately detected in different initial scales. The proposed method greatly reduces the data processing time compared with the coastline detection method based on a single scale.

Automatic pharynx and larynx cancer segmentation framework (PLCSF) on contrast enhanced MR images

A novel and effective pharynx and larynx cancer segmentation framework (PLCSF) is presented for automatic base of tongue and larynx cancer segmentation from gadolinium-enhanced T1-weighted magnetic resonance images (MRI). The aim of the proposed PLCSF is to assist clinicians in radiotherapy treatment planning. The initial processing of MRI data in PLCSF includes cropping of region of interest; reduction of artefacts and detection of the throat region for the location prior. Further, modified fuzzy c-means clustering is developed to robustly separate candidate cancer pixels from other tissue types. In addition, region-based level set method is evolved to ensure spatial smoothness for the final segmentation boundary after noise removal using non-linear and morphological filtering. Validation study of PLCSF on 102 axial MRI slices demonstrate mean dice similarity coefficient of 0.79 and mean modified Hausdorff distance of 2.2 mm when compared with manual segmentations. Comparison of PLCSF with other algorithms validates the robustness of the PLCSF. Inter- and intra-variability calculations from manual segmentations suggest that PLCSF can help to reduce the human subjectivity.

Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method

Image segmentation is still an open problem due to the existing of intensity inhomogeneity and noise. To accurately segment images with these biases, a local inhomogeneous intensity clustering (LINC) model is proposed. In LINC, a linear combination of a given set of smooth orthogonal basis functions is used to estimate the bias field. A local clustering criterion function is first defined to cluster the nearly homogeneous intensities in a relatively small neighborhood of each pixel. An energy functional is then defined by integrating the function with respect to the neighborhood center. This energy together with a regularization term and an arc length term are incorporated into a variational level set formulation in which de-nosing is implicitly included due to the implied convolution. Image segmentation and bias correction can be simultaneously achieved by updating variables of the final energy functional iteratively till it is stable or a predetermined iteration number is reached. The proposed model LINC has been extensively tested on both synthetic and real images. Experimental results and comparison with state-of-the-art methods demonstrate the advantages of the proposed model in terms of segmentation accuracy, bias field correction, dealing with noise, and robustness to initialization.

Active contour driven by multi-scale local binary fitting and Kullback-Leibler divergence for image segmentation

Image segmentation is an important processing in many applications such as image retrieval and computer vision. One of the most successful models for image segmentation is the level set methods which are based on local context. The methods, though comparatively effective in segmenting images with inhomogeneous intensity, are considerably computation-intensive and at the risk of falling into local minima in the convergence of the active contour energy function. To address the issues, we propose a region-based level set method, called KL-MLBF, which is based on the multi-scale local binary fitting (MLBF) and the Kullback-Leibler (KL) divergence. We first apply the multi-scale theory to the local binary fitting model to build MLBF. Then the energy term measured by KL divergence between regions to be segmented is incorporated into the energy function of MLBF. KL-MLBF utilizes the between-cluster distance and the adaptive kernel function selection strategy to formulate the energy function. Being more robust to the initial location of the contour than the classical segmentation models, KL-MLBF can deal with blurry boundaries and noise problems. The results of experiments on synthetic and real images have shown that KL-MLBF can improve the effectiveness of segmentation while ensuring the accuracy by accelerating the minimization of the energy function.

Level set method for image segmentation based on moment competition

We propose a level set method for image segmentation which introduces the moment competition and weakly supervised information into the energy functional construction. Different from the region-based level set methods which use force competition, the moment competition is adopted to drive the contour evolution. Here, a so-called three-point labeling scheme is proposed to manually label three independent points (weakly supervised information) on the image. Then the intensity differences between the three points and the unlabeled pixels are used to construct the force arms for each image pixel. The corresponding force is generated from the global statistical information of a region-based method and weighted by the force arm. As a result, the moment can be constructed and incorporated into the energy functional to drive the evolving contour to approach the object boundary. In our method, the force arm can take full advantage of the three-point labeling scheme to constrain the moment competition. Additionally, the global statistical information and weakly supervised information are successfully integrated, which makes the proposed method more robust than traditional methods for initial contour placement and parameter setting. Experimental results with performance analysis also show the superiority of the proposed method on segmenting different types of complicated images, such as noisy images, three-phase images, images with intensity inhomogeneity, and texture images.

Segmentation of mass in mammograms by a novel integrated active contour method

Mass segmentation plays a vital role in the computer aided diagnosis systems for breast cancer. To efficiently detect the true boundaries of mass regions, a fully automated, dual-stage method was developed. Firstly an improved region-based level set method was applied for coarse segmentation and the gradient information was integrated to avoid boundary leaking. The obtained rough contour was used as an initial boundary for further refinement. In order to detect the delicate margins which are of significant meaning for breast diagnosis, a local geodesic active contour LGAC model based on local image information was proposed to refine the rough contour. The experimental results suggested that the proposed improved level set method can correctly find the radial and ambiguous edges of mass regions. Compared to the classical level set methods, the new scheme is more accurate and robust for mass segmentation in mammograms.