Mortensen, E., Morse, B., Barrett, W., Udupa, J.: Adaptive boundary detection using live-wire two-dimensional dynamic programming. In: IEEE Proceedings of Computers in Cardiology. (1992) 635--638  Home/Search   Document Not in Database   Summary   Related Articles   Check

....deformable contour model are achieved by discrete Euler equation, which involves iterative computations. Another approach of the deformable contour model, is to form this problem as a two dimensional graph search problem, using dynamic programming(Dijkstra [14] as in [3, 10] the live wire in [15], and intelligent scissors in [35, 36] This approach gives the user a larger control over the segmentation process. Once a start point is selected, an optimal path can be computed and drawn in real time between the start point and any given position. In the intelligent scissors model, the ....

W.A. Barrett E.N. Mortensen, B.S. Morse and J.K. Udupa. Adaptive boundary detection using `live-wire' two-dimensional dynamic programming. In IEEE Proceedings of Computers in Cardiology, Oct. 1992. 130

....tdeschamps lbl.gov Ceremade, Universite Paris 9 Dauphine, 75775 Paris Cedex 16, France. cohen ceremade.dauphine.fr Abstract. The aim of this work is to propose an adaptation of optimal path based interactive tools for image segmentation (related to Live Wire [12] and Intelligent Scissors [18] approaches) We e#ciently use both discrete [10] and continuous [6] path search approaches. The segmentation relies on the notion of energy function and we introduce the possibility of complete on the fly adaptation of each individual energy term, as well as of their relative weights. ....

....minima by using either dynamic programming (Dijkstra [10] or a front propagation equation (Cohen and Kimmel [6] mapping the non convex cost function into a function with only one minimum. Falcao and Udupa with their Live Wire [11, 12] and Mortensen and Barrett with their Intelligent Scissors [18 21] introduced interactivity into the optimal path approach. Their method is based on Dijkstra s graph search algorithm and gives to the user a large control over the segmentation process. The idea is the following: a start point is selected by the user on the boundary to be extracted, and an optimal ....

E. Mortensen, B. Morse, W. Barrett, J.K. Udupa, "Adaptive boundary detection using live-wire two-dimensional dynamic programming", in IEEE Proc. of Computers in Cardiology, pp. 635--638, October 1992.

....interesting venues for a scientific understanding of the prediction method. The NNSA method is conceptually simple and has rather low computational complexity. C. 1 Liver Segmentation To extract the liver L, we developed semiautomatic seg mentation tools (based on live wire techniques [50] [31], 10] With live wire segmentation the user starts with se lecting a first contour point and moves a pointing device (for example a mouse) to roughly sketch the object s con tour. The algorithm relies on a cost function to calculate an optimal path between the start point and the current ....

Mortensen, EN, Morse, BS, Barrett WA, Udupa JK, Adaptive boundary detection using live-wire two-dimensional dynamic programming, IEEE Computers in Cardiology (Durham, North Carolina), IEEE Computer Society Press, 1992, pp. 635-638.

....photographs, videos and volumetric image data. Despite decades of research there is still a lack of satisfying approaches to the segmentation of arbitrary images, videos or volumentric data. Therefore some semi automatic approaches have been suggested, e.g. by Fischler [1] Chien [2] Mortensen [3, 4, 5] and others. Semi automatic approaches take into account that the true outline of an object cannot always be found by analyzing a specific neighborhood of pixels or voxels but that previous knowledge of typical object shapes also has to be included. The user defines points along the object ....

....the necessary segmentation information that can be derived from the image itself and the knowledge that can only be externally introduced by the semantic knowledge of the user. 2. BRIEF OUTLINE OF LIVE WIRE AND PRECEDING WORK Semi automatic live wire segmentation was introduced by Mortensen [3, 4, 5] and Barret [6] in a variety of implementations with differing cost functions and performances. The basic idea is to define a so called seed point in the image. As the user moves the mouse, a trajectory (the wire) is drawn from the position of the cursor back to the seed point. Thereby the wire is ....

E. N. Mortensen, B. S. Morse, W. A. Barrett, Adaptive Boundary Detection Using 'Live-Wire' Two-Dimensional Dynamic Programming, IEEE Proceedings of Computer in Cardiology (CIC '92), pp. 635-638, Durham, NC, Oct. 1992

....similar to the boundary of the LV (the endocardium) and then refine this estimation to better fit the data [San95a] Usually these techniques miss a significant part of the myocardium, since not every slice that intersects the myocardium also intersects the LV. Urschler et al. used the Live Wire [Mor92a] technique for segmentation of the LV [Urs02a] Their method works well distinguishing the LV from the left chamber of the heart, which is a tough problem as outlined in section 6. This, however, is achieved at the cost of a big amount of interaction needed for segmenting each time step. They do ....

Mortensen, E., N., Morse, B., S., Barrett, W., A., and Udupa, J., K. Adaptive Boundary Detection Using `Live Wire' Two-Dimensional Dynamic Programming. In IEEE Proceedings of Computers in Cardiology (CIC '92), pp. 635-638, Durham, NC, Oct. 1992

....and the surface, the user will edit the surface until the desired shape is obtained. The concept behind the proposed environment is depicted in Fig. 1. Attempts to develop user guided and interactive segmentation methods have been made before. Barrett, Udupa, Mortensen, and others [1] 7] 17] [18] [20] developed a method known as live wire with which a user roughly draws the boundary of a region of interest using a mouse. An automatic process then takes over and revises the boundary by optimizing 2 a cost function. An alternative method was introduced where the user selects a number of ....

E. N. Mortensen, B. S. Morse, W. A. Barrett, and J. K. Udupa, Adaptive boundary detection using live-wire two-dimensional dynamic programming, IEEE Proc. Computers in Cardiology, 1992, pp. 635-638.

....(1) With such a cost function, edge tracking can be considered as a minimal cost path search problem between two points in the image graph. Fischler et al. 1981 ] used the well known F # algorithm to solve this minimisation problem in a semi automatic framework for road detection, whereas [ Mortensen et al. 1992 ] applied the very similar A # algorithm for interactive segmentation of medical imagery. Interaction possibilities are limited to the choice of start and end points of the path. Subsequent modifications of the shape can only be accomplished by moving each single polygonal vertex. In order to ....

E. N. Mortensen, B. Morse, and W. A. Barret. Adaptive boundary detection using live-wire two-dimensional dynamic programming. Computers in Cardiology, pages 635--638, October 1992.

....completely in finding at least an approximation of the correct object boundary. If a certain application asks for absolutely flawless segmentations, alternative or supplemental frameworks must be applied to compensate for the missing functionality. On the one hand, we could employ semi automatic [6, 10, 17, 8] or manual segmentation tools that rely on a human operator providing the missing information. On the other hand, we may initialise the fully automatic procedure such that the correct solution is just nearby the initial configuration. Since almost all semi automatic methods rely on suitable ....

E. N. Mortensen, B. Morse, and W. A. Barret. Adaptive boundary detection using live-wire two-dimensional dynamic programming. Computers in Cardiology, pages 635--638, October 1992.

....stages of the algorithms (this includes a multiscale strategy and various modifications to the basic algorithm) Previous work: Our proposed problem has similar characteristics as the original work of Montanari[21] and followed by Wu and Maitre [26] Ballard and Brown [17] Mortensen et. al [22], Sashua and Ullman [23] and Barzohar and Cooper[19] Geiger et al. [20] The use of a more efficient algorithm, Dijkstra algorithm, is new in this domain. Also the heuristics are particular to this application. 2.1 Intensity Correction The mass of the fragments are estimated under the following ....

E. Mortensen, B. Morse, W. Barret, and J. Udupa, 1992. Adaptive boundary detection using live-wire two dimensional dynamic programming, In , Computers and Cardiology, pp:635-638.

....this author, along with Dr. William Barrett, introduced a unique boundary based segmentation technique called Intelligent Scissors [110,112] which allows rapid and accurate object extraction using simple gestures motions with a mouse. Based on the live wire interactive optimal path selection tool [10 11,109,111], Intelligent Scissors performs general purpose, interactive object segmentation by allowing the user to choose a minimum cost contour segment corresponding to a portion of the desired object boundary. As the mouse position comes in proximity to an object edge, a live wire boundary snaps to and ....

....edges that exhibit a point spread (i.e. blur) of less than a pixel will be effectively ignored. Further, since low(p)andhigh(p) do not readily generalize to multi banded (i.e. color) data, the DCE is not well suited for this dissertation. DCE p ( min p low p ( high p ( p , Like [111] and [112] this work measures discontinuity or gradient magnitude using a multi scale derivative of Gaussian operator that automatically adapts, on a pixel bypixel basis, to the point spread of each image object [57,79] A derivative of Gaussian filter is used due to the Gaussian s localization ....

[Article contains additional citation context not shown here]

E. N. Mortensen, Adaptive Boundary Detection Using 'Live-Wire' Two-Dimensional Dynamic Programming. Masters Thesis, Department of Computer Science, Brigham Young University, Provo, UT, Aug. 1995.

....this author, along with Dr. William Barrett, introduced a unique boundary based segmentation technique called Intelligent Scissors [110,112] which allows rapid and accurate object extraction using simple gestures motions with a mouse. Based on the live wire interactive optimal path selection tool [10 11,109,111], Intelligent Scissors performs general purpose, interactive object segmentation by allowing the user to choose a minimum cost contour segment corresponding to a portion of the desired object boundary. As the mouse position comes in proximity to an object edge, a live wire boundary snaps to and ....

....square. While compromising optimality, the advantage of the adaptive live lane is that it resorts to a manual tracing tool when the mouse is moved slowly, thus producing an effective mechanism for overriding the live wire tool. Finally, they use the wrap around bucket sort technique introduced in [10 11,109 112] to create their ultrafast implementation of live wire on the fly [5354 ] Like [50,52] other implementations of live wire Intelligent Scissors also restrict the graph search to a region around the mouse cursor [69] or by pruning during the graph expansion such that two consecutive edges in ....

[Article contains additional citation context not shown here]

E. N. Mortensen, B. S. Morse, W. A. Barrett, and J. K. Udupa, "Adaptive Boundary Detection Using 'Live-Wire' Two-Dimensional Dynamic Programming," in IEEE Proc. of Computers in Cardiology, pp. 635-638, Durham, NC, Oct. 1992.

....techniques. As a practical matter, live wire boundary snapping typically requires less time and effort to accurately segment an object than it takes to manually input an initial approximation to the object boundary. Live wire boundary snapping for image segmentation was introduced initially in (Mortensen et al. 1992; Udupa et al. 1992) This paper reports on the application of the live wire technology to medical images and its performance in terms of the speed, accuracy, and reproducibility with which boundaries can be extracted (Barrett and Mortensen, 1996) In addition, several significant contributions ....

Mortensen, E.N., Morse, B.S. and Barrett, W.A. (1992) Adaptive Boundary Detection Using `Live-Wire' Two-Dimensional Dynamic Programming. IEEE Computers in Cardiology, pp. 635638.

No context found.

Mortensen, E., Morse, B., Barrett, W., Udupa, J.: Adaptive boundary detection using live-wire two-dimensional dynamic programming. In: IEEE Proceedings of Computers in Cardiology. (1992) 635--638

No context found.

E. Mortensen, B. Morse, W. Barrett, and J. Udupa, "Adaptive boundary detection using live-wire two-dimensional dynamic programming," Computers in Cardiology, pp. 635--638, 1992.

No context found.

E. Mortensen, B. Morse, W. Barrett, and J. Udupa, "Adaptive boundary detection using live-wire two-dimensional dynamic programming," Comput. Cardiol., pp. 635--638, 1992.

No context found.

E. Mortensen, B. Morse, W. Barrett, and J. Udupa, "Adaptive boundary detection using live-wire two-dimensional dynamic programming," Comput. Cardiology, pp. 635--638, 1992.

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