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- W2356875114 abstract "The segmentation of abdominal CT series is a challenging task due to problems such as blur edges,large variance among individuals and small sample sizes.In this paper,a hybrid 3D surface segmentation algorithm based on a multi-resolution integrated model and missing data recovery technique is proposed.The appearance models to characterize the texture features around surface points are established,and theconfidence level(CFL)for each point is defined.For the points which have high confidence,segmentation is accomplished by active image searching and model deformation.While for the points which have low confidence,instead of using unreliable edge information,data recovery technique is applied based on a statistical deformable model and available high confidence points.The experimental results demonstrate that the Hybrid-MISTO achieves the lowest segmentation error compared with a variety of state-of-the-art techniques such as Snake,ASM,and MISTO." @default.
- W2356875114 created "2016-06-24" @default.
- W2356875114 creator A5071285943 @default.
- W2356875114 date "2010-01-01" @default.
- W2356875114 modified "2023-09-25" @default.
- W2356875114 title "An Abdominal Image Segmentation Algorithm based on Multi-resolution Statistical Model and Surface Recovery" @default.
- W2356875114 hasPublicationYear "2010" @default.
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