A novel approach for curve evolution in segmentation of medical images (original) (raw)
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Effective Image Segmentation using Composite Energy Metric in Levelset Based Curve Evolution
January 2019, 2019
Accurate segmentation of anatomical organs in medical images is a complex task due to wide inter-patient variability and several acquisition dependent artefacts. Moreover, image noise, low contrast and intensity inhomogeneity in medical data further amplifies the challenge. In this work, we propose an effective yet simple algorithm based on composite energy metric for precise detection of object boundaries. A number of methods have been proposed in literature for image segmentation; however, these methods employ individual characteristics of image including gradient, regional intensity or texture map. Segmentation based on individual featres often fail for complex images, especially for medical imagery. Accordingly, we propose that the segmentation quality can be improved by integrating local and global image features in the curve evolution. This work employs the classic snake model aka active contour model; however, the curve evolution force has been updated. In contast to the conv...
Area prior constrained level set evolution for medical image segmentation
Medical Imaging 2008: Image Processing, 2008
The level set framework has proven well suited to medical image segmentation 1-6 thanks to its ability of balancing the contribution of image data and prior knowledge in a principled, flexible and transparent way. It consists of evolving a curve toward the target object boundaries. The curve evolution equation is sought following the optimization of a cost functional containing two types of terms: data terms, which measure the fidelity of segmentation to image intensities, and prior terms, which traduce learned prior knowledge. Without priors many algorithms are likely to fail due to high noise, low contrast and data incompleteness. Different priors have been investigated such as shape 1 and appearance priors. 7 In this study, we propose a simple type of priors: the area prior. This prior embeds knowledge of an approximate object area and has two positive effects. First, It speeds up significantly the evolution when the curve is far from the target object boundaries. Second, it slows down the evolution when the curve is close to the target. Consequently, it reinforces curve stability at the desired boundaries when dealing with low contrast intensity edges. The algorithm is validated with several experiments using Magnetic Resonance (MR) images and Computed Tomography (CT) images. A comparison with another level set method illustrates the positive effects of the area prior.
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.
Area prior constrained level set evolution for medical image segmentation
Medical Imaging 2008: Image Processing, 2008
The level set framework has proven well suited to medical image segmentation 1-6 thanks to its ability of balancing the contribution of image data and prior knowledge in a principled, flexible and transparent way. It consists of evolving a curve toward the target object boundaries. The curve evolution equation is sought following the optimization of a cost functional containing two types of terms: data terms, which measure the fidelity of segmentation to image intensities, and prior terms, which traduce learned prior knowledge. Without priors many algorithms are likely to fail due to high noise, low contrast and data incompleteness. Different priors have been investigated such as shape 1 and appearance priors. 7 In this study, we propose a simple type of priors: the area prior. This prior embeds knowledge of an approximate object area and has two positive effects. First, It speeds up significantly the evolution when the curve is far from the target object boundaries. Second, it slows down the evolution when the curve is close to the target. Consequently, it reinforces curve stability at the desired boundaries when dealing with low contrast intensity edges. The algorithm is validated with several experiments using Magnetic Resonance (MR) images and Computed Tomography (CT) images. A comparison with another level set method illustrates the positive effects of the area prior.
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.
IEEE Transactions on Image Processing, 2000
In this paper, we present a novel information theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution, and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use fast level set methods to implement the resulting evolution. The evolution equations are based on nonparametric statistics, and have an intuitive appeal. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems.
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...
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in . Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework.