Contour Model For Image Segmentation Research Articles

A New Local Region-Based Active Contour Model for Image Segmentation Based on Adaptive Double Potential Well Function

A New Local Region-Based Active Contour Model for Image Segmentation Based on Adaptive Double Potential Well Function

ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

An active contour model for image segmentation using morphology and nonlinear Poisson’s equation

Active contour model (ACM) is considered as a feasible tool to handle image segmentation problems via an unsupervised learning approach. This paper compensates the shortcomings of well-known ACMs to introduce erosion and dilation operations in mathematical morphology to fit the intensity differential functions of contours on both sides of the image. A nonlinear Poisson’s equation is developed to deal with the interference of nonuniform noise within the image, and finally build a new model. This paper develops a new image processing approach using morphology and nonlinear Poisson’s equation, which yields a fast and efficient algorithm among known similar image segmentation methods, and has a high level of accuracy in processing image segmentation.

Adaptive Image Segmentation for Traumatic Brain Haemorrhage

It is challenging to establish a significant solution with computer techniques to improve the speed and efficiency of Traumatic Brain Injury (TBI) diagnosis. Several segmentation methods involving diverse precision and a degree of effort have been proposed and detailed within the related literature. Segmentation of Brain image is one of the significant clinical diagnostics implements. This paper proposes a modified (MDRLSE) calculation for haemorrhage segmentation on Computed Tomography (CT) images. The image noise that abdicates the obscured edges is utilized to portray the precise boundary of the haemorrhage region. The proposed segmentation technique achieved an accuracy rate of 97.16%. The technique is implemented using an edge-based involved contour model for image segmentation, providing a simple narrowband to significantly reduce computational costs. The performance results show that it is effective for TBI image segmentation in brain images with various characteristics.

Adaptive Image Segmentation for Traumatic Brain Haemorrhage

It is challenging to establish a significant solution with computer techniques to improve the speed and efficiency of Traumatic Brain Injury (TBI) diagnosis. Several segmentation methods involving diverse precision and a degree of effort have been proposed and detailed within the related literature. Segmentation of Brain image is one of the significant clinical diagnostics implements. This paper proposes a modified (MDRLSE) calculation for haemorrhage segmentation on Computed Tomography (CT) images. The image noise that abdicates the obscured edges is utilized to portray the precise boundary of the haemorrhage region. The proposed segmentation technique achieved an accuracy rate of 97.16%. The technique is implemented using an edge-based involved contour model for image segmentation, providing a simple narrowband to significantly reduce computational costs. The performance results show that it is effective for TBI image segmentation in brain images with various characteristics.

Integrated vector-valued active contour model for image segmentation

Integrated vector-valued active contour model for image segmentation

Active Contour Model for Image Segmentation With Dilated Convolution Filter

Active Contour Model for Image Segmentation With Dilated Convolution Filter

Active contour model for image segmentation based on salient fitting energy

Although segmentation of image is very important in disease diagnosis, there still exist some difficult problems to precise segmentation, such as noise and intensity inhomogeneity. Aiming at these issues, a novel level set algorithm based on salient fitting energy is presented. We firstly transform original image into a new modality which utilises greyscale change characteristics of a local area. Secondly, a data term of salient fitting energy can be constructed by solving the deviation between new modality and input images in a neighbourhood. In addition, distance regularised term is introduced in the proposed method to remove the re-initialisation process. The experiment on a lot of medical and synthetic images demonstrate that the proposed method has good segmentation ability on the part of visual perception.

Active contour model for image segmentation based on salient fitting energy

Active contour model for image segmentation based on salient fitting energy

Fuzzy region-based active contour driven by global and local fitting energy for image segmentation

This paper presents a novel global and local fuzzy image fitting (GLFIF) based active contour model for image segmentation. First, we design two fitted images: global fuzzy fitted image (GFFI) and local fuzzy fitted image (LFFI). Next, the energy function including a global term and a fitting term is constructed. The global term comes from the fuzzy energy-based active contour (FEAC) model to balance the importance of the object and background while the fitting term is based on GFFI and LFFI to handle the intensity inhomogeneity in given images. Then, we prove the energy function to be convex, which can ensure the segmentation results independent of initialization. Finally, unlike the FEAC model by computing the change of pixel-by-pixel energy function, a direct method is utilized to calculate the difference between the old and new energy functions in the whole image for each iteration to update the pseudo level set function. Experiments on synthetic and real images show that the proposed model is more robust than the popular active contour models for segmenting the images with noise, blurred boundaries, and intensity inhomogeneity. The code is available at: https://github.com/fangchj2002/GLFIF.

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.

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.

State-of-the-art fuzzy active contour models for image segmentation

Image segmentation is the initial step for every image analysis task. A large variety of segmentation algorithm has been proposed in the literature during several decades with some mixed success. Among them, the fuzzy energy-based active contour models get attention to the researchers during last decade which results in development of various methods. A good segmentation algorithm should perform well in a large number of images containing noise, blur, low contrast, region in-homogeneity, etc. However, the performances of the most of the existing fuzzy energy-based active contour models have been evaluated typically on the limited number of images. In this article, our aim is to review the existing fuzzy active contour models from the theoretical point of view and also evaluate them experimentally on a large set of images under the various conditions. The analysis under a large variety of images provides objective insight into the strengths and weaknesses of various fuzzy active contour models. Finally, we discuss several issues and future research direction on this particular topic.

Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation

Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods.

Saliency-Driven Active Contour Model for Image Segmentation

Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models.

A novel local region-based active contour model for image segmentation using Bayes theorem

Local region-based active contour methods have been widely used to segment images with intensity inhomogeneity. However, this process can hardly segment images well when influenced by different noise. To simultaneously strengthen the anti-noise ability and preserve the distinction in segmenting images with intensity inhomogeneity, we propose characterizing image regions using local prior region descriptors under the Bayesian criterion for image segmentation. Based on the framework of Bayes theorem, a spatial regularization of connectivity maps based on a Markov random field (MRF) is introduced as the prior probability in our model. The connectivity maps enhance noise robustness by building a relationship between a pixel and its adjacent pixels. Additionally, the conditional probability of the image intensity in each local region is assumed to satisfy a Gaussian distribution with different means and deviations. Furthermore, to decrease time costs, we use the sparse field method (SFM) and compute the means and variances of the intensities in each local region before the evolution of the contour. Extensive experiments have demonstrated that the proposed method is superior to state-of-the-art active contour methods in terms of time efficiency and noise robustness.

Active contour model based on variable exponent p-Laplace equation for image segmentation

ABSTRACTIn this paper, we propose a region-based active contour model for image segmentation. By combining the region fitting energy based on coefficient of variation with the variable exponent p-Laplace energy, the proposed method can perform well in segmenting complex images. The region fitting energy conducts the evolving curve to reach the boundaries of the objects, and the p-Laplace energy can handle the topological changes and extract the boundaries accurately. In order to eliminate the re-initialization step, an augmented Lagrangian method is employed to solve the optimization problem. The results of experiments on synthetic and real images demonstrate that our method can successfully segment complex object boundaries, and it is robust to noise and not sensitive to the initial position of contours.

An Adaptive Stopping Active Contour Model for Image Segmentation

Active contour models (ACMs) are widely used in image segmentation applications. However, the selection of maximum iterations which controls the convergence of the ACMs is still a challenging problem. In this paper, an adaptive method for choosing the optimal number of iterations based on the local and global intensity fitting energy is proposed, which increases the automaticity of the active contour model. Moreover, the adoption of the reaction diffusion (RD) method instead of the distance regularization term can improve the accuracy and speed of segmentation effectively. Experimental results on synthetic and real images show that the proposed model outperforms other representative models in terms of accuracy and efficiency.

结合Retinex校正和显著性的主动轮廓图像分割

结合Retinex校正和显著性的主动轮廓图像分割

Active contours driven by non-local Gaussian distribution fitting energy for image segmentation

Image segmentation is still a challenging task in image processing field because of unpredictable noise and intensity inhomogeneity in images. In this paper, we present a novel active contour model for image segmentation by constructing a robust truncated kernel function. It utilizes image patches to perceive the neighborhood intensities of pixel at the same time considers the spatial distance within a local window. By using this truncated kernel function, the proposed method can accurately segment images with intensity inhomogeneity while guaranteeing certain noise robustness. Extensive evaluations on synthetic and real images are provided to demonstrate the superiority of our method. The model makes full use of image patch information to strengthen the robustness against noise and intensity inhomogeneity in images.