Segmentation of biomedical images using active contour model with robust image feature and shape prior (original) (raw)
Related papers
2016
Medical images segmentation due to the increasing volume of this images is difficult and in accessible to human. with development of image processing we can use computers to help. Segmentation of the early stages of image processing are very well regarded. In this paper, a combination method based on level set and active contour models have been proposed to achieve more accurate image in image processing. The selected dataset contains different slices that in this article 8 slices have been selected for testing. The proposed algorithm was applied on these slices and it has been compared with previous methods. The results have been tested on MRI images of the brain and this results show that the proposed method is better than other methods proposed before.
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.
Shape prior based on statistical map for active contour segmentation
2008
We propose a new method for performing active contour segmentation based on the statistical prior knowledge of the object to detect. From a binary training set of objects, a statistical map describes the possible shapes of the object by computing the probability for each point to belong to the object. This statistical map is treated as a prior distribution and an energy functional is de ned such that the object reaches the most probable shape knowing the model. The optimization is done in the level-set framework. Results on both synthetic and medical images are shown.
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 shape-based approach to the segmentation of medical imagery using level sets
IEEE Transactions on Medical Imaging, 2003
We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras [15], we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.
Variational and Shape Prior-based Level Set Model for Image Segmentation
2010
A new image segmentation model based on level sets approach is presented herein. We deal with radiographic medical images where boundaries are not salient, and objects of interest have the same gray level as other structures in the image. Thus, an a priori information about the shape we look for is integrated in the level set evolution for good segmentation results. The proposed model also accounts a penalization term that forces the level set to be close to a signed distance function (SDF), which then avoids the re-initialization procedure. In addition, a variant and complete Mumford-Shah model is used in our functional; the added Hausdorff measure helps to better handle zones where boundaries are occluded or not salient. Finally, a weighted area term is added to the functional to make the level set drive rapidly to object's boundaries. The segmentation model is formulated in a variational framework, which, thanks to calculus of variations, yields to partial differential equations (PDEs) to guide the level set evolution. Results obtained on both synthetic and digital radiographs reconstruction (DRR) show that the proposed model improves on existing prior and non-prior shape based image segmentation.
Statistical region-based active contours for segmentation: An overview
IRBM, 2014
In this paper we propose a brief survey on geometric variational approaches and more precisely on statistical region-based active contours for medical image segmentation. In these approaches, image features are considered as random variables whose distribution may be either parametric, and belongs to the exponential family, or non-parametric estimated with a kernel density method. Statistical region-based terms are listed and reviewed showing that these terms can depict a wide spectrum of segmentation problems. A shape prior can also be incorporated to the previous statistical terms. A discussion of some optimization schemes available to solve the variational problem is also provided. Examples on real medical images are given to illustrate some of the given criteria.
Active Contour Segmentation with a Parametric Shape Prior: Link with the Shape Gradient
2006 International Conference on Image Processing, 2006
Active contours are adapted to image segmentation by energy minimization. The energies often exhibit local minima, requiring regularization. Such an a priori can be expressed as a shape prior and used in two main ways: (1) a shape prior energy is combined with the segmentation energy into a trade-off between prior compliance and accuracy or (2) the segmentation energy is minimized in the space defined by a parametric shape prior. Methods (1) require the tuning of a data-dependent balance parameter and methods (1) and (2) are often dedicated to a specific prior or contour representation, with the prior and segmentation aspects often meshed together, increasing complexity. A general framework for category (2) is proposed: it is independent of the prior and contour representations and it separates the prior and segmentation aspects. It relies on the relationship shown here between the shape gradient, the prior-induced admissible contour transformations, and the segmentation energy minimization.
Level set segmentation with robust image gradient energy and statistical shape prior
2011
We propose a new level set segmentation method with statistical shape prior using a variational approach. The image energy is derived from a robust image gradient feature. This gives the active contour a global representation of the geometric configuration, making it more robust to image noise, weak edges and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the model to handle relatively large shape variations. Comparative examples using both synthetic and real images show the robustness and efficiency of the proposed method.
Medical image segmentation based on level set method
This paper presents a shape-based approach to curve evolution for the segmentation of medical images. Automatic interpretation of medical images is a very difficult problem in computer vision. Several methods have been developed in last decade to improve the segmentation performance in computer vision. A promising mathematical framework based on variational models and partial differential equations has been investigated to solve the image segmentation problem. This approach benefits from well-established mathematical theories that allow people to analyze, understand and extend segmentation methods. In this paper, a variational formulation is considered to the segmentation using active contours models.