Fast Level Set Research Articles

A Fast Level-Set Curve-Flow Formulation by Logarithmic Step-Size Control for Brain Ultrasound Image Segmentation

A Fast Level-Set Curve-Flow Formulation by Logarithmic Step-Size Control for Brain Ultrasound Image Segmentation

A fast level set image segmentation driven by a new region descriptor

In order to deal with the intensities inhomogeneities and to overcome the effect of different types of noise in the image segmentation process, we have formulated a new level set function to implement a fast and robust active contour model. The proposed model was formulated by combining the SBGFRLS model and Legendre polynomials. With the aim of ensuring the segmentation accuracy and for dealing in the best way with the presence of noises and inhomogenous distribution of intensity, we define a local region descriptor for image intensities based on the Legendre polynomial. Instead of using the average intensity of the region, we regularised our level set function using a Gaussian filtering process. Experimental results on challenging images demonstrate the efficiency, robustness and the low cost and computational time of our model against the well-known active contour models.

Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method

Background and ObjectiveBrain tumor segmentation is a challenging issue due to noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual MRI segmentation is a very tedious, time-consuming, and user-dependent task. This paper aims to presents a novel level set method to address aforementioned challenges for reliable and automatic brain tumor segmentation. MethodsIn the proposed method, a new functional, based on level set method, is presented for medical image segmentation. Firstly, we define a superpixel fuzzy clustering objective function. To create superpixel regions, multiscale morphological gradient reconstruction (MMGR) operation is used. Secondly, a novel fuzzy energy functional is defined based on superpixel segmentation and histogram computation. Then, level set equations are obtained by using gradient descent method. Finally, we solve the level set equations by using lattice Boltzmann method (LBM). To evaluate the performance of the proposed method, both synthetic image dataset and real Glioma brain tumor images from BraTS 2017 dataset are used. ResultsExperiments indicate that our proposed method is robust to noise, initialization, and intensity non-uniformity. Moreover, it is faster and more accurate than other state-of-the-art segmentation methods with the averages of running time is 3.25 seconds, Dice and Jaccard coefficients for automatic tumor segmentation against ground truth are 0.93 and 0.87, respectively. The mean value of Hausdorff distance, Mean absolute Distance (MAD), accuracy, sensitivity, and specificity are 2.70, 0.005, 0.9940, 0.9183, and 0.9972, respectively. ConclusionsOur proposed method shows satisfactory results for Glioma brain tumor segmentation due to superpixel fuzzy clustering accurate segmentation results. Moreover, our method is fast and robust to noise, initialization, and intensity non-uniformity. Since most of the medical images suffer from these problems, the proposed method can more effective for complicated medical image segmentation.

3-D Level Set Method for Joint Contrast and Shape Recovery in Microwave Imaging

We propose a three-dimensional (3-D) fast level set method, which can estimate both object shape and dielectric contrast with reduced computational cost. In prior work, we presented a 2-D fast level set method that integrated the level set inversion within the Born iterative method (BIM). This approach significantly reduced the computational cost; however, it was limited to estimating object shape without providing any dielectric contrast inversion. In this paper, we extend our previous method to 3-D and add the capability to estimate dielectric contrast in addition to the shape. The contrast estimation is formulated as a separate step within the BIM iteration, such that the shape and contrast are estimated sequentially in each BIM iteration. This modular framework enables the freedom to choose any arbitrary constraints on the contrast in the cost function. Here, we demonstrate the applicability of a total-variation constraint for the contrast cost function. To validate the method, synthetic data are generated for objects within a 3-D imaging cavity and are then used to test for robustness against variations in object shape, size, position, and contrast. Further, the method is tested on MRI-derived numerical breast phantoms. The reconstructed images indicate that the method can produce accurate estimates of object location, shape, and size while recovering the contrast with an error lower than $\text{2}\%$ .

Layer boundary evolution method for macular OCT layer segmentation.

Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra- and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.

Fast Two-Cycle Level Set Tracking With Interactive Superpixel Segmentation and Its Application in Image Retrieval

In this paper, a fast two-cycle level set contour tracking algorithm based on interactive superpixel segmentation is proposed. Then, image retrieval is carried out based on the contour tracking results. The method of contour tracking is divided into three stages: superpixel segmentation, interactive feature extraction, and foreground tracking. In the first phase, the image is preprocessed by a superpixel segmentation method. In the second stage, the feature pools are defined and established interactively according to the complexity of the image. In the last stage, based on the first two stages, a fast two-cycle level set method is used to track the contour of the target object. Finally, the results of the contour tracking are applied to an image matting of the target foreground, and then the foreground image retrieval is implemented. The results of experimental comparisons demonstrate the effectiveness of the proposed method.

Underwater image segmentation based on fast level set method

Image segmentation is a fundamental process in image processing that has found application in many fields, such as neural image analysis, and underwater image analysis. In this paper, we propose a novel fast level set method (FLSM)-based underwater image segmentation method to improve the traditional level set methods by avoiding the calculation of signed distance function (SDF). The proposed method can speed up the computational complexity without re-initialisation. We also provide a fast semi-implicit additive operator splitting (AOS) algorithm to improve the computational complex. The experiments show that the proposed FLSM performs well in selecting local or global segmentation regions.

Underwater image segmentation based on fast level set method

Underwater image segmentation based on fast level set method

A fast level set model of multi‐atlas labels fusion for 3D MRI tissues segmentation with application of split Bregman method

In this paper, we propose a new fast level set model of multi‐atlas labels fusion for 3D magnetic resonance imaging (MRI) tissues segmentation. The proposed model is aimed at segmenting regions of interest in MR images, especially the tissues such as the amygdala, the caudate, the hippocampus, the pallidum, the putamen, and the thalamus. We first define a new energy functional by taking full advantage of an image data term, a length term, and a label fusion term. Different from using the region‐scalable fitting image data term and length term directly, we define a new image data term and a new length term, which is also incorporated with an edge detect function. By introducing a spatially weight function into the label fusion term, segmentation sensitivity to warped images can be largely improved. Furthermore, the special structure of the new energy functional ensures the application of the split Bregman method, which is a significant highlight and can improve segmentation efficiency of the proposed model. Because of these promotions, several good characters, such as accuracy, efficiency, and robustness have been exhibited in experimental results. Quantitative and qualitative comparisons with other methods have demonstrated the superior advantages of the proposed model.

Road Scene Simulation Based on Vehicle Sensors: An Intelligent Framework Using Random Walk Detection and Scene Stage Reconstruction.

Road scene model construction is an important aspect of intelligent transportation system research. This paper proposes an intelligent framework that can automatically construct road scene models from image sequences. The road and foreground regions are detected at superpixel level via a new kind of random walk algorithm. The seeds for different regions are initialized by trapezoids that are propagated from adjacent frames using optical flow information. The superpixel level region detection is implemented by the random walk algorithm, which is then refined by a fast two-cycle level set method. After this, scene stages can be specified according to a graph model of traffic elements. These then form the basis of 3D road scene models. Each technical component of the framework was evaluated and the results confirmed the effectiveness of the proposed approach.

Analysis on the Internet public opinions in the era of We media based on fast dual-cycle level set

ABSTRACT A classified prediction model for Internet public opinions (IPO) with data mining (DM) technology based on the theory of fast dual-cycle level set is proposed in this paper to improve IPO prediction precision. Firstly, a feature extraction algorithm is applied to model the weighting of IPO feature values, which reflect its significance in IPO. Then IPO similarity is used to present the degree of similarity between two IPO topics with respect to semantic meaning. At last, a certain hot topic on the Internet is used for simulation test on the model proposed. The results show that the model proposed herein can accurately predict the trends of IPO changes and improve the IPO prediction precision.

Multi-layer Fast Level Set Segmentation for Macular OCT.

Segmenting optical coherence tomography (OCT) images of the retina is important in the diagnosis, staging, and tracking of ophthalmological diseases. Whereas automatic segmentation methods are typically much faster than manual segmentation, they may still take several minutes to segment a three-dimensional macular scan, and this can be prohibitive for routine clinical application. In this paper, we propose a fast, multi-layer macular OCT segmentation method based on a fast level set method. In our framework, the boundary evolution operations are computationally fast, are specific to each boundary between retinal layers, guarantee proper layer ordering, and avoid level set computation during evolution. Subvoxel resolution is achieved by reconstructing the level set functions after convergence. Experiments demonstrate that our method reduces the computation expense by 90% compared to graph-based methods and produces comparable accuracy to both graph-based and level set retinal OCT segmentation methods.

Hybrid fitting energy‐based fast level set model for image segmentation solving by algebraic multigrid and sparse field method

A novel hybrid fitting energy-based active contours model in the level set framework is proposed. The method fuses the local image fitting term and the global image fitting term to drive the contour evolution, and a special extra term that penalises the deviation of the level set function from a signed distance function is also included in the authors' method, so the complex and costly reinitialisation procedure is completely eliminated. Their model can efficiently segment the images with intensity inhomogeneity no matter where the initial curve is located in the image. In its numerical implementation, two efficient numerical schemes are used to ensure the sufficient efficiency of the evolution process, one is the algebraic multigrid, which is used for breaking the restrictions on time step; the other is the sparse field method, which is introduced for fast local computation, compared with the traditional schemes, these two strategies can further shorten the time consumption of the evolution process, this allows the level set to quickly reach the true target location. The extensive and promising experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.

A fast and robust level set motion-assisted deformable registration method for volumetric CT guided lung intervention

This paper describes the accurate deformable registration method for image-guided lung interventions, including lung nodule biopsy and radiofrequency ablation of lung tumours. A level set motion assisted deformable registration method for computed tomography (CT) images was proposed and its accuracy and speed were compared with those of other conventional methods. Fifteen 3D CT images obtained from lung biopsy patients were scanned. Each scan consisted of diagnostic and preoperative CT images. Each deformable registration method was initially evaluated with a landmark-based affine registration algorithm. Various deformable registration methods such as level set motion, demons, diffeomorphic demons, and b-spline were compared. Visual assessment by two expert thoracic radiologists using five scales showed an average visual score of 3.2 for level set motion deformable registration, whereas scores were below 3 for other deformable registration methods. In the qualitative assessment, the level set motion algorithm showed better results than those obtained with other deformable registration methods. A level set motion based deformable registration algorithm was effective for registering diagnostic and preoperative volumetric CT images for image-guided lung intervention.

A Fast Level Set Method for Multi-Material Recovery in Microwave Imaging

Level set-based methods for microwave imaging can produce better results than linearized approaches but they are usually very slow because they require many iterations of a forward solver. In this paper, we have developed an approach, where we need only a small number of forward solver runs, to reconstruct the microwave images accurately. Our approach is a combination of a level set method and a linearized approach and has a regularization term for stability during level set iterations. We provide a detailed analysis of the method and demonstrate its applicability on synthetically generated data for 2-D microwave imaging. Different objects chosen to represent realistic features are considered to evaluate the performance. This approach is extended to multiple materials. The reconstructed images indicate that the method can produce accurate object localization, and shape and size identification. It can also recover the shape very well even for high contrast and heterogeneous structures.

An improved fast level set method initialized with a combination of k-means clustering and Otsu thresholding for unsupervised change detection from SAR images

Detection of changes in synthetic aperture radar (SAR) images is an important challenge due to the effects of speckle noise on these images. In recent years, appropriate methods for SAR-based-change detection have been developed based on the level set methods (LSM). These methods need to set parameters for defining a proper initial contour. Moreover, the gradient information is only employed in the total energy of these methods for segmentation of the difference image. In this study, a novel method has been proposed for unsupervised change detection of multitemporal SAR images based on the improved fast level set method (IFLSM) initialized with a combination of k-means and Otsu techniques. The proposed method utilizes the discrete wavelet transform (DWT) fusion strategy and edge enhancement to achieve a noise-resistant difference image from the mean-ratio and log-ratio images. Afterward, the generated binary change map (CM) by applying a combination of k-means and Otsu techniques on the difference image is used as the initial contour to achieve a final CM on difference image using the IFLSM. To check advantages of the proposed method, experiments are applied on two sets of multitemporal SAR images corresponding to artificial Chitgar Lake (under reconstruction) in Tehran (Iran) taken by TerraSAR-X satellite in 2011 and 2012, and corresponding to San Pablo and Briones reservoirs in California (USA) acquired by ERS-2 satellite in 2003 and 2004. Results of proposed method were compared with results of some well-known unsupervised change detection methods. Experimental results prove the sufficiency of the proposed method in unsupervised change detection in terms of accuracy, implementation time, and computational complexity.

Fast Hybrid Level Set Model for Non-homogenous Image Segmentation Solving by Algebraic Multigrid

A novel hybrid fitting energy based active contours model in the level set framework is proposed. The method fuses the local image fitting term and the global image fitting term to drive the contour evolution, and a special extra term that penalizes the deviation of the level set function from a signed distance function is also included in our method, so the complex and costly re-initialization procedure is completely eliminated. Our model can efficiently segment the images with intensity inhomogeneity no matter where the initial curve is located in the image. In its numerical implementation, the Algebraic Multigrid (AMG) is used for breaking the restrictions on time step, compared with the traditional schemes, the AMG strategy can shorten the time consumption of the evolution process, this allows the level set to quickly reach the true target location. The extensive and promising experimental results on numerous synthetic and real images have shown that our method can efficiently improve the image segmentation performance, in terms of accuracy, efficiency, and robustness.

Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing

Performing tasks with a robot hand often requires a complete knowledge of the manipulated object, including its properties (shape, rigidity, surface texture) and its location in the environment, in order to ensure safe and efficient manipulation. While well-established procedures exist for the manipulation of rigid objects, as well as several approaches for the manipulation of linear or planar deformable objects such as ropes or fabric, research addressing the characterization of deformable objects occupying a volume remains relatively limited. The paper proposes an approach for tracking the deformation of non-rigid objects under robot hand manipulation using RGB-D data. The purpose is to automatically classify deformable objects as rigid, elastic, plastic, or elasto-plastic, based on the material they are made of, and to support recognition of the category of such objects through a robotic probing process in order to enhance manipulation capabilities. The proposed approach combines advantageously classical color and depth image processing techniques and proposes a novel combination of the fast level set method with a log-polar mapping of the visual data to robustly detect and track the contour of a deformable object in a RGB-D data stream. Dynamic time warping is employed to characterize the object properties independently from the varying length of the tracked contour as the object deforms. The proposed solution achieves a classification rate over all categories of material of up to 98.3%. When integrated in the control loop of a robot hand, it can contribute to ensure stable grasp, and safe manipulation capability that will preserve the physical integrity of the object.

Wound intensity correction and segmentation with convolutional neural networks

SummaryWound area changes over multiple weeks are highly predictive of the wound healing process. A big data eHealth system would be very helpful in evaluating these changes. We usually analyze images of the wound bed for diagnosing injury. Unfortunately, accurate measurements of wound region changes from images are difficult. Many factors affect the quality of images, such as intensity inhomogeneity and color distortion. To this end, we propose a fast level set model‐based method for intensity inhomogeneity correction and a spectral properties‐based color correction method to overcome these obstacles. State‐of‐the‐art level set methods can segment objects well. However, such methods are time‐consuming and inefficient. In contrast to conventional approaches, the proposed model integrates a new signed energy force function that can detect contours at weak or blurred edges efficiently. It ensures the smoothness of the level set function and reduces the computational complexity of re‐initialization. To increase the speed of the algorithm further, we also include an additive operator‐splitting algorithm in our fast level set model. In addition, we consider using a camera, lighting, and spectral properties to recover the actual color. Numerical synthetic and real‐world images demonstrate the advantages of the proposed method over state‐of‐the‐art methods. Experimental results also show that the proposed model is at least twice as fast as methods used widely. Copyright © 2016 John Wiley & Sons, Ltd.

Fast two-cycle curve evolution with narrow perception of background for object tracking and contour refinement

The problem of object contour tracking in image sequences remains challenging, especially those with cluttered backgrounds. In this paper, the fast two-cycle level set method with narrow perception of background (FTCNB) is proposed to extract the foreground objects, e.g. vehicles from road image sequences. The curve evolution of the level set method is implemented by computing the signs of region competition terms on two linked lists of contour pixels rather than by solving partial differential equations (PDEs). The curve evolution process mainly consists of two cycles: one cycle for contour pixel evolution and a second cycle for contour pixel smoothness. Based on the curve evolution process, we introduce two tracking stages for the FTCNB method. For coarse tracking stage, the speed function is defined by region competition term combining color and texture features. For contour refinement stage which requires higher tracking accuracy, the likelihood models of the Maximum a posterior (MAP) expressions are incorporated for the speed function. Both the tracking and refinement stages utilize the fast two-cycle curve evolution process with the narrow perception of background regions. With these definitions, we conduct extensive experiments and comparisons for the proposed method. The comparisons with other baseline methods well demonstrate the effectiveness of our work.