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|Type:||Artigo de periódico|
|Title:||Fuzzy-connected 3D image segmentation at interactive speeds|
|Abstract:||Image segmentation techniques using fuzzy connectedness principles hake shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem with these algorithms has been their excessive computational requirements. In an attempt to substantially speed them up. in the present paper, we study systematically a host of 18 'optimal' graph search algorithms. Extensive testing of these algorithms on a variety of 3D medical images taken from large ongoing applications demonstrates that a 20 1000-fold improvement over current speeds is achievable with a combination of algorithms and last modern PCs. Utilizing efficient algorithms and careful selection of implementations can speed up the computation of fuzzy connectedness values by a factor of 16 29 (on the same hardware), as compared to the implementation previously used in our applications utilizing fuzzy object segmentation. The optimality of an algorithm depends on the input data as well as on the choice of the fuzzy affinity relation. The running time is reduced considerably (by a factor up to 34 for brain MR and even more for bone CT), when the algorithms make use of predetermined thresholds for the fuzz), objects. The reliable recognition (assisted by human operators) and the accurate, efficient. and sophisticated delineation (automatically performed by the computer) can be effectively incorporated into a single interactive process. If images having intensities kith tissue-Specific meaning (such Lis CT or standardized MR images) are utilized. most of the parameters for the segmentation method can be fixed once for all. all, intermediate data (feature and fuzzy affinity values for the hole scene) can be computed before the user interaction is needed and the user can be provided kith more information at the little of interaction. (C) 2003 Elsevier Science (USA). All rights reserved.|
|Editor:||Academic Press Inc Elsevier Science|
|Citation:||Graphical Models. Academic Press Inc Elsevier Science, v. 64, n. 5, n. 259, n. 281, 2002.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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