Active contours driven by local and global fitted image models for image segmentation robust to intensity inhomogeneity (original) (raw)
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IEEE Transactions on Instrumentation and Measurement
Image segmentation is a tedious task that suffers from constraints, such as blurred or weak edges and intensity inhomogeneity. Active contour models, including edge-based and region-based methods, are extensively used for image segmentation. Each of these methods has its pros and cons that affect image-segmentation accuracy and CPU processing time. This study combines local and global region-based fitting energies and uses statistical image information to drag contours toward object boundaries, thus overcoming image inhomogeneity. The bias field, the region affected by image artefacts, is calculated and added with the local fitting energy model to capture inhomogeneous object boundaries. Furthermore, the combined local and global statistical information is appended with the edge-indicator function to rapidly move the contour over objects with strong edges, thereby avoiding boundary leakage. A regionbased length term is driven by the signed pressure force function that evolves the curve on either the outer or inner side of the object, depending on its sign. The signed pressure force function contributes to achieving a smoother version of energy minimization over gradient descent flow. The proposed active contour model is applied to multiple synthetically generated, and medical images, together with online available public databases: the PH 2 database, the skin-cancer-mnist-ham10000 THUS10000 database and the specific images from PascalVOC2007 database [1]. All the experiments confirm the better segmentation accuracy and improved time potency of the proposed methodology over previous level set-based approaches.
PLOS ONE, 2018
This paper presents a novel two-stage image segmentation method using an edge scaled energy functional based on local and global information for intensity inhomogeneous image segmentation. In the first stage, we integrate global intensity term with a geodesic edge term, which produces a preliminary rough segmentation result. Thereafter, by taking final contour of the first stage as initial contour, we begin second stage segmentation process by integrating local intensity term with geodesic edge term to get final segmentation result. Due to the suitable initialization from the first stage, the second stage precisely achieves desirable segmentation result for inhomogeneous image segmentation. Two stage segmentation technique not only increases the accuracy but also eliminates the problem of initial contour existed in traditional local segmentation methods. The energy function of the proposed method uses both global and local terms incorporated with compacted geodesic edge term in an additive fashion which uses image gradient information to delineate obscured boundaries of objects inside an image. A Gaussian kernel is adapted for the regularization of the level set function and to avoid an expensive re-initialization. The experiments were carried out on synthetic and real images. Quantitative validations were performed on Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2015 and PH 2 skin lesion database. The visual and quantitative comparisons will demonstrate the efficiency of the proposed method.
Novel Convex Active Contour Model Using Local and Global Information
2011
In this paper, we propose a novel region-based active contour model for image segmentation. Our model incorporates the global and local information in the energy function, enabling efficient segmentation of images while accounting for intensity inhomogeneity. Another interesting property of the proposed model is its convexity, making it independent of the initial condition and hence ideal for an automatic segmentation. Furthermore, the energy function of the proposed model is minimized in a computationally efficient way by using the Chambolle method. Experimental results on natural and medical images demonstrate the performance of our model over the current state-of-the-art.
Active Contour Model for Image Segmentation With Dilated Convolution Filter
IEEE Access
ACMs have been demonstrated to be highly suitable as image segmentation models for computer vision tasks. Among other ACM, the local region-based models show better performance because they extract the local information regarding intensity in the neighborhood and embed it into the energy minimization function to guide the active contour to the boundary of the desired object. However, the online segmentation of noisy and inhomogeneous is still a challenging task for local region-based ACM models. To overcome this challenge, the paper proposes a novel region-based active contour model, named active contour model with local dilated convolution filter (ACLD). The ACLD integrates local image information in the form of a signed pressure force function. Then, a Gaussian kernel is applied using dilated convolution instead of discrete convolution for regularizing the level set formulation. Finally, instead of using a constant stopping condition, the ACLD automatically stops at the object boundaries. The proposed model shows improved image segmentation results visually combined with less computational time in the case of synthetic and natural images compared with the state-of-the-art models. Further, on the ISIC2017 dataset, the ACLD yields segmentation results with the highest accuracy. INDEX TERMS Active contours, intensity inhomogeneity, image segmentation, level set method. I. INTRODUCTION
Global and local fuzzy energy-based active contours for image segmentation
Nonlinear Dynamics
This paper proposes a novel active contour model for image segmentation based on techniques of curve evolution. The paper introduces an energy functional including a local fuzzy energy and a global fuzzy energy to attract the active contour and stop it on the object boundaries. The local term allows the method to deal with intensity inhomogeneity in images. The global term, aside from driving the contour toward the desired objects, is used to avoid unsatisfying results led by unsuitable initial contour position, a common limitation of models using local information solely. In addition, instead of solving the Euler–Lagrange equation, the paper directly calculates the alterations of the fuzzy energy. By this way, the contour converges quickly to the object boundary. Experimental results on both 2D and 3D images validate the effectiveness of the model when working with intensity inhomogeneous images.
Computational and Mathematical Methods in Medicine, 2020
Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over ...
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing, 2010
A novel region-based active contour model (ACM) is proposed in this paper. It is implemented with a special processing named Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method, which first selectively penalizes the level set function to be binary, and then uses a Gaussian smoothing kernel to regularize it. The advantages of our method are as follows. First, a new region-based signed pressure force (SPF) function is proposed, which can efficiently stop the contours at weak or blurred edges. Second, the exterior and interior boundaries can be automatically detected with the initial contour being anywhere in the image. Third, the proposed ACM with SBGFRLS has the property of selective local or global segmentation. It can segment not only the desired object but also the other objects. Fourth, the level set function can be easily initialized with a binary function, which is more efficient to construct than the widely used signed distance function (SDF). The computational cost for traditional re-initialization can also be reduced. Finally, the proposed algorithm can be efficiently implemented by the simple finite difference scheme. Experiments on synthetic and real images demonstrate the advantages of the proposed method over geodesic active contours (GAC) and Chan-Vese (C-V) active contours in terms of both efficiency and accuracy.
Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation
IEEE Access, 2020
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-ofthe-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. INDEX TERMS Active contour, intensity inhomogeneity, image segmentation, region-based, local and global intensity.
Local image fitting-based active contour for vector-valued images
Indonesian Journal of Electrical Engineering and Computer Science
Variational active contour seeks to segment or extract desired object boundaries for further analysis. The model can be divided into global segmentation and selective segmentation. Selective segmentation, which focuses on segmenting a particular object, is preferable to the global model. Recently, a number of selective segmentation models have been developed to precisely extract an object on grayscale images. Nevertheless, if the input image is vector-valued (colour), these models merely convert it to a grayscale image, resulting in data loss owing to the reduction in image dimension. Furthermore, they may have poor segmentation performance due to the intensity inhomogeneous images. Therefore, a new model on variational selective active contour for segmenting vector-valued images has been proposed that incorporates the concepts of local image fitting and distance-based fitting terms into a variational minimization energy functional. Moreover, a Gaussian function was used as a regula...