Medical image segmentation based on level set method (original) (raw)
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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.
A novel approach for curve evolution in segmentation of medical images
Computerized Medical Imaging and Graphics, 2010
A new joint parametric and nonparametric curve evolution algorithm is proposed for medical image segmentation. In this algorithm, both the nonlinear space of level set function (nonparametric model) and the linear subspace of level set function spanned by the principle components (parametric model) are employed in the evolution procedure. The nonparametric curve evolution can drive the curve precisely to object boundaries while the parametric model acts as a statistical constraint based on the Bayesian framework in order to match object shape more robustly. As a result, our new algorithm is as robust as the parametric curve evolution algorithms and at the same time, yields more accurate segmentation results by using the shape prior information. Comparative results on segmenting ventricle frontal horns and putamen shapes in MR brain images confirm the advantages of the proposed joint curve evolution algorithm.
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.
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...
Adaptive segmentation algorithm based on level set model in medical imaging
TELKOMNIKA, 2023
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
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.
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.
On the Relationship between Variational Level Set-Based and SOM-Based Active Contours
Computational Intelligence and Neuroscience, 2015
Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.
Active contours of biomedical imaging
The active contour models are widely used for segmentation. The Biomedical images require quality segmentation that can only be provided by active contours plus we need computational complexity as well. In this paper, a new model is proposed for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford Shah functional and level sets. Those objects can also be detected whose gradient is not necessarily defined. Energy is minimized that can be particular case of minimal partition problem. Stopping term is related to particular segmentation rather instead of dependent on gradient of image. At last, various experimental results are presented and the differences of this model with other techniques are also elaborated. The initial curve can be anywhere in the image and interior contours are automatically detected.