An approach to CT stomach image segmentation using modified level set method
19 Mar 2012-pp 227-233
TL;DR: A modified version of the level set method is employed to segment the stomach from CT images and the results show that the algorithm is able to detect inner boundaries in the considered CT stomach images and it appears that it is also possible to extract outer boundaries as well.
Abstract: Internal organs of a human body have very complex structure owing to their anatomic organization. Several image segmentation techniques fail to segment the various organs from medical images due to simple biases. Here, a modified version of the level set method is employed to segment the stomach from CT images. Level set is a model based segmentation method that incorporates a numerical scheme. For the sake of stability of the evolving zero'th level set contour, instead of periodic reinitialization of the signed distance function, a distance regularization term is included. This term is added to the energy optimization function which when solved with gradient flow algorithms, generates a solution with minimum energy and maximum stability. Evolution of the contour is controlled by the edge indicator function. The results show that the algorithm is able to detect inner boundaries in the considered CT stomach images. It appears that it is also possible to extract outer boundaries as well. The results of this approach are reported in this paper.
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Patent•
14 Jul 2015
TL;DR: In this article, a method for automated detection of illegal substances smuggled inside internal cavities of a passenger, e.g., as capsules, was proposed, using pictures produced by an X-ray scanner.
Abstract: A method for automated detection of illegal substances smuggled inside internal cavities of a passenger, e.g., as capsules. The method provides for an automated detection of narcotics hidden in a passenger's stomach area using pictures produced by an X-ray scanner. According to an exemplary embodiment, throughput of the scanner is increased by an automated detection algorithm, which takes less time than visual analysis by an operator. The operator is only involved in cases when narcotics are detected. The automated detection method has a consistent precision, because the effects of tiredness of the operator are eliminated. Efficiency and costs of the process are improved, since fewer qualified operators can service several scanners.
6 citations
01 May 2014
TL;DR: An attempt has been made to detect the IMT of far wall, using level set segmentation method based on edge map without re-initialisation, which shows that the proposed method is able to extract the boundary of farwall despite the presence of noise in ultrasound images.
Abstract: Carotid wall analysis using ultrasound image is a challenging task for the diagnosis of cardiovascular pathologies. Intima-Media Thickness (IMT) is the marker of the progression of atheroscelerosis in carotid arteries which leads to stroke. In this paper, an attempt has been made to detect the IMT of far wall, using level set segmentation method based on edge map without re-initialisation. Geometric features such as equivalent diameter, solidity and extent are extracted from the normal and abnormal images. Results show that the proposed method is able to extract the boundary of far wall despite the presence of noise in ultrasound images. The features show distinct variation between normal and abnormal images. Its values are higher in abnormal compared to normal images. Among all the features, extent shows high variation between normal and abnormal images. Hence, this method of analysis could be used in early identification of cardiovascular diseases.
4 citations
Cites methods from "An approach to CT stomach image seg..."
...initialisation of the signed distance function is employed in CT liver images [9] and phase based level set segmentation technique applied [10] in delineating the liver using CT images....
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06 Nov 2014
TL;DR: The edge based variational level set method is able to segment the intima-media layer precisely from common carotid artery precisely and seems to be clinically useful in diagnosis of cardiovascular disease.
Abstract: In this work an attempt has been made to enhance the edges and segment the boundary of intima-media layer of Common Carotid Artery (CCA) using anisotropic diffusion filter and level set method. Ultrasound B mode longitudinal images of normal and abnormal images of common carotid arteries are used in this study. The images are subjected to anisotropic diffusion filter to generate edge map. This edge map is used as a stopping boundary in variational level set method without re-initialisation to segment the intima-media layer. Geometric features are extracted from this layer and analyzed statistically. Results show that anisotropic diffusion filtering is able to extract the edges in both normal and abnormal images. The obtained edge maps are found to have high contrast and sharp edges. The edge based variational level set method is able to segment the intima-media layer precisely from common carotid artery. The extracted geometrical features such as major axis and extent are found to be statistically significant in differentiating normal and abnormal images. Thus this study seems to be clinically useful in diagnosis of cardiovascular disease.
3 citations
Cites methods from "An approach to CT stomach image seg..."
...Level set methods have been adopted to segment the liver images [18, 19]....
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TL;DR: This paper proposes automatic segmentation of the upper digestive tract from abdominal contrast-enhanced CT using previously segmented multiple organs and compared segmentation accuracy of the esophagus, stomach and duodenum between the proposed method using hierarchical statistical modeling and a conventional statistical atlas method.
Abstract: We have been studying the automatic segmentation of multi-organ region from abdominal CT images. In previous work, we proposed an approach using a hierarchical statistical modeling using a relationship between organs. In this paper, we have proposed automatic segmentation of the upper digestive tract from abdominal contrast-enhanced CT using previously segmented multiple organs. We compared segmentation accuracy of the esophagus, stomach and duodenum between our proposed method using hierarchical statistical modeling and a conventional statistical atlas method. Additionally, preliminary experiment was performed which added the region representing gas to the candidate region at the segmentation step. The segmentation results were evaluated quantitatively by Dice coefficient, Jaccard index and the average symmetric surface distance of the segmented region and correct region data. Percentage of the average of Dice coefficient of esophagus, stomach and duodenum were 58.7, 68.3, and 38.6 with prediction-based method and 23.7, 51.1, and 24.4 with conventional atlas method.
1 citations
Cites background from "An approach to CT stomach image seg..."
...The automatic segmentation of digestive tract has not been much proposed, only those that were preliminary experiment or its accuracy was not high enough [6, 7]....
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References
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TL;DR: The PSC algorithm as mentioned in this paper approximates the Hamilton-Jacobi equations with parabolic right-hand-sides by using techniques from the hyperbolic conservation laws, which can be used also for more general surface motion problems.
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13,020 citations
TL;DR: In this article, the authors proposed a shape model based on the Hamilton-Jacobi approach to shape modeling, which retains some of the attractive features of existing methods and overcomes some of their limitations.
Abstract: Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This paper presents a new approach to shape modeling which retains some of the attractive features of existing methods and overcomes some of their limitations. The authors' techniques can be applied to model arbitrarily complex shapes, which include shapes with significant protrusions, and to situations where no a priori assumption about the object's topology is made. A single instance of the authors' model, when presented with an image having more than one object of interest, has the ability to split freely to represent each object. This method is based on the ideas developed by Osher and Sethian (1988) to model propagating solid/liquid interfaces with curvature-dependent speeds. The interface (front) is a closed, nonintersecting, hypersurface flowing along its gradient field with constant speed or a speed that depends on the curvature. It is moved by solving a "Hamilton-Jacobi" type equation written for a function in which the interface is a particular level set. A speed term synthesized from the image is used to stop the interface in the vicinity of object boundaries. The resulting equation of motion is solved by employing entropy-satisfying upwind finite difference schemes. The authors present a variety of ways of computing the evolving front, including narrow bands, reinitializations, and different stopping criteria. The efficacy of the scheme is demonstrated with numerical experiments on some synthesized images and some low contrast medical images. >
3,039 citations
TL;DR: A new variational level set formulation in which the regularity of the level set function is intrinsically maintained during thelevel set evolution called distance regularized level set evolution (DRLSE), which eliminates the need for reinitialization and thereby avoids its induced numerical errors.
Abstract: Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.
1,947 citations
TL;DR: In this paper, a coupled level set method for the motion of multiple junctions (of, e.g., solid, liquid, and grain boundaries), which follows the gradient flow for an energy functional consisting of surface tension and bulk energies, is developed.
Abstract: A coupled level set method for the motion of multiple junctions (of, e.g., solid, liquid, and grain boundaries), which follows the gradient flow for an energy functional consisting of surface tension (proportional to length) and bulk energies (proportional to area), is developed. The approach combines the level set method of S. Osher and J. A. Sethian with a theoretical variational formulation of the motion by F. Reitich and H. M. Soner. The resulting method uses as many level set functions as there are regions and the energy functional is evaluated entirely in terms of level set functions. The gradient projection method leads to a coupled system of perturbed (by curvature terms) Hamilton?Jacobi equations. The coupling is enforced using a single Lagrange multiplier associated with a constraint which essentially prevents (a) regions from overlapping and (b) the development of a vacuum. The numerical implementation is relatively simple and the results agree with (and go beyond) the theory as given in 12. Other applications of this methodology, including the decomposition of a domain into subregions with minimal interface length, are discussed. Finally, some new techniques and results in level set methodology are presented.
1,158 citations
Book•
14 Dec 2011
TL;DR: This book discusses methods for preserving geometric deformable models for brain reconstruction, as well as methods for implicit active contour models, and some of the methods used in this book were adapted for this purpose.
Abstract: * Level set methods * Deformable models * Fast methods for implicit active contour models * Fast edge integration * Variational snake theory * Multiplicative denoising and deblurring * Total varation minimization for scalar/vector regularization * Morphological global reconstruction and levelings * Fast marching techniques for visual grouping and segmentation * Multiphase object detection and image segmentation * Adaptive segmentation of vector-valued images * Mumford-Shah for segmentation and stereo * Shape analysis toward model-based segmentation * Joint image registration and segmentation * Image alignment * Variational principles in optical flow estimation and tracking * Region matching and tracking under deformations or occlusions * Computational stereo * Visualization, analysis and shape reconstruction of sparse data * Variational problems and partial differential equations on implicit surfaces * Knowledge-based segmentation of medical images * Topology preserving geometric deformable models for brain reconstruction * Editing geometric models * Simulating natural phenomena
899 citations