Multiscale image segmentation using active contours (original) (raw)
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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.
Image Segmentation Based on Hybrid Adaptive Active Contour
Lecture Notes in Computer Science, 2015
In this paper, we focus on segmentation based active contour model. In fact, we present an hybrid adaptive active contour segmentation algorithm. In this approach, we merge a global and an adaptive local based active contour models in order to segment images. The proposed energy is then minimized based on level set method. Experiments shows the good segmentation results provided by the proposed method.
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.
Image segmentation based on active contours without edges
2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, 2012
There are a lot of image segmentation techniques that try to differentiate between background and object pixels, but many of them fail to discriminate between different objects that are close to each other. Some image characteristics like low contrast between background and foreground or inhomogeneity within the objects increase the difficulty of correctly segmenting images. We designed a new segmentation algorithm based on active contours without edges. We also used other image processing techniques such as nonlinear anisotropic diffusion and adaptive thresholding in order to overcome the images' problems stated above. Our algorithm was tested on very noisy images, and the results were compared to those obtained with known methods, like segmentation using active contours without edges and graph cuts. The new technique led to very good results, but the time complexity was a drawback. However, this drawback was significantly reduced with the use of graphical programming. Our segmentation method has been successfully integrated in a software application whose aim is to segment the bones from CT datasets, extract the femur and produce personalized prostheses in hip arthroplasty.
Active Contours for Multi-region Image Segmentation with a Single Level Set Function
Lecture Notes in Computer Science, 2013
Segmenting the image into an arbitrary number of parts is at the core of image understanding. Many formulations of the task have been suggested over the years. Among these are axiomatic functionals, which are hard to implement and analyze, while graph-based alternatives impose a non-geometric metric on the problem. We propose a novel approach to tackle the problem of multiple-region segmentation for an arbitrary number of regions. The proposed framework allows generic region appearance models while avoiding metrication errors. Updating the segmentation in this framework is done by level set evolution. Yet, unlike most existing methods, evolution is executed using a single non-negative level set function, through the Voronoi Implicit Interface Method for a multi-phase interface evolution. We apply the proposed framework to synthetic and real images, with various number of regions, and compare it to state-of-the-art image segmentation algorithms.
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...
PloS one, 2017
This paper presents a region-based active contour method for the segmentation of intensity inhomogeneous images using an energy functional based on local and global fitted images. A square image fitted model is defined by using both local and global fitted differences. Moreover, local and global signed pressure force functions are introduced in the solution of the energy functional to stabilize the gradient descent flow. In the final gradient descent solution, the local fitted term helps extract regions with intensity inhomogeneity, whereas the global fitted term targets homogeneous regions. A Gaussian kernel is applied to regularize the contour at each step, which not only smoothes it but also avoids the computationally expensive re-initialization. Intensity inhomogeneous images contain undesired smooth intensity variations (bias field) that alter the results of intensity-based segmentation methods. The bias field is approximated with a Gaussian distribution and the bias of intensi...
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.
Bi-planar image segmentation based on variational geometrical active contours with shape priors
Medical Image Analysis, 2013
This work proposes an image segmentation model based on active contours. For a better handling of regions where anatomical structures are poorly contrasted and/or missing, we propose to incorporate a priori shape information in a variational formulation. Based on a level set approach, the proposed functional is composed of four terms. The first one makes the level set keep the important signed distance function property, which is necessary to guarantee the good level set evolution. Doing so results in avoiding the classical re-initialization process, contrary to most existing works where a partial differential equation is used instead. The second energy term contains the a priori information about admissible shapes of the target object, the latter being integrated in the level set evolution. An energy that drives rapidly the level set towards objects of interest is defined in the third term. A last term is defined on prior shapes thanks to a complete and modified Mumford-Shah model. The segmentation model is derived by solving the Euler-Lagrange equations associated to the functional minimization. Efficiency and robustness of our segmentation model are validated on synthetic images, digitally reconstructed images, and real image radiographs. Quantitative evaluations of segmentation results are also provided, which also show the importance of prior shapes in the context of image segmentation.
A VARIATIONAL SEGMENTATION FRAMEWORK USING ACTIVE CONTOURS AND THRESHOLDING
2007
Segmentation involves separating distinct regions in an image. In this note, we present a novel variational approach to perform this task. We propose an energy functional that naturally combines two segmentation techniques usually applied separately: intensity thresholding and geometric active contours. Although our method can deal with more complex image statistics, intensity averages are used to separate regions, in this present work. The proposed approach affords interesting properties that can lead to sensible segmentation results.