A Novel Global Threshold-Based Active Contour Model (original) (raw)
Related papers
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.
A novel active contour model for medical image segmentation
Journal of Shanghai Jiaotong University (Science), 2010
A novel segmentation method for medical image with intensity inhomogeneity is introduced. In the proposed active contour model, both region and gradient information are taken into consideration. The former, i.e., region-based fitting energy, draws upon the region information and guarantees the accurate extraction of inhomogeneous image's local information. The latter, i.e., an edge indicator, weights the length penalizing term to consider the gradient constrain. Moreover, signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set function. Experiments for synthetic and real images demonstrate the feasibility and superiority of the proposed model.
Role of Active Contour in Image Processing
2014
With the growing research on image segmentation, it has become important to provide readers with an overview of the existing segmentation techniques. The active contour is one of the most successful models in image segmentation. Active contours are used extensively in computer vision and digital image processing to locate object boundaries. It consists of evolving a contour in images toward the boundaries of objects. Its success is based on strong mathematical properties and efficient numerical schemes based on the level set method. In this paper, we review and classify active contour models in literature.
International journal of scientific research in computer science, engineering and information technology, 2017
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.
Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation
Edge-based active contour models are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as in medical images. Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we propose a framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which incorporates gradient information as well as probability scores from a standard classifier, the ESF can be constructed from any classification algorithm and applied to any edge-based model using a level set method. Experiments on medical images using the distance regularized level set for edge-based active contour models as well as the k-nearest neighbours and the support vector machine confirm the effectiveness of the proposed approach. Source code and animation are available at the link below: http://pratondo.staff.telkomuniversity.ac.id/2016/01/14/robust-edge-stop-functions-for-edge-based-active-contour-models-in-medical-image-segmentation/
Detection of Liver cancer from CT images is an exigent task due to the reason that cancer impression in CT images are of very low in contrast and have indistinguishable edges. Indistinguishable edges are edges in which foreground and background are almost same. Image segmentation is used to extract desired anatomical structure from image. Image segmentation is the process of dividing the image into multiple regions. These regions are sometimes called region of interest (ROI). These ROI's are used as informative inputs to further image processing e.g. feature extraction, selection and ultimately the classification of a disease. Thus an effective image segmentation is utmost important in medical images. In case of indistinguishable edges most segmentation techniques fails to detect edges. Edge based and region based active contour methods are most prevalent for image segmentation. Edge based technique works using gradient function and ultimately stopping function to detect edges , while region based uses average information inside and outside regions to control evolution and termination at edges. In this paper comparative analysis of edge based and region based active contour using level sets is done. When applying these methods on the CT images with an impression of liver cancer, it has been found that the edge based contour able to locate the desired edges more accurately. The quantitative and qualitative results comparison between two techniques has also been done. Results shows that edge based methods performs comparatively better than region based active contour using level set if number of iterations are controlled properly.
Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2017
Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow fi...
A New Region-based Active Contour Model for Object Segmentation
Journal of Mathematical Imaging and Vision, 2015
We present a novel region based active contour model that segments one or more image regions that are visually similar to an object of interest, said prior. The region evolution equation of our model is defined by a simple heuristic rule and it is not derived by minimizing an energy functional, as in the classic variational approaches. The prior and the evolving region are described by the probability density function (pdf) of a photometric feature, as color or intensity. The heuristic rule enlarges or contracts an initial region of the image in order to equalize pointwise the pdf's of the prior and of the region. Such heuristic rule can be modeled by many mathematical monotonic decreasing functions, each defining an evolution equation for the initial image region. The choice of a particular function is remitted to the user, that in this way can even integrate a priori knowledge possibly useful to break down the computational charge of the method and to increase the detection accuracy. Here we propose two different evolution equations for the general purpose of prior detection without a priori information and we discuss empirically the performances of our model on real-world and synthetic datasets. These experiments show that our model is a valid alternative to the classic models.
Image Segmentation using Active Contours Without Edge
In this paper, we propose a segmentation method based on an active contour model without edges which, given an input image or image sequence, generates a mask of the desired object(s). This model is then extended to be used in multi-object tracking. Our method allows us to detect objects which are not necessarily delimited by gradient. In other terms, we do not need to detect edges of the object we want to extract from the image to perform our segmentation. To perform this segmentation, we base our model on techniques of curve evolution, the Mumford-Shah functional and level sets. Our problem can be seen as a particular case of the minimal partition problem in minimizing energy domain. The goal of this survey is also to present an interactive and parametrizable software which helps the understanding of the model and allows to test its limits. After presenting our model, we will give the numerical algorithms and the discrete approximations we used. Finally, various experimental results will be presented and commented.
Active Contours and Image Segmentation: The Current State Of the Art
Global journal of computer science and technology, 2012
Image segmentation is a fundamental task in image analysis responsible for partitioning an image into multiple sub-regions based on a desired feature. Active contours have been widely used as attractive image segmentation methods because they always produce sub-regions with continuous boundaries, while the kernel-based edge detection methods, e.g. Sobel edge detectors, often produce discontinuous boundaries. The use of level set theory has provided more flexibility and convenience in the implementation of active contours. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. Here in this paper we attempt to brief the taxonomy and current state of the art in Image segmentation and usage of Active Contours.