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Titlebook: Medical Computer Vision; Recognition Techniqu Bjoern Menze,Georg Langs,Antonio Criminisi Conference proceedings 2011 Springer Berlin Heidel

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Localization of 3D Anatomical Structures Using Random Forests and Discrete Optimization problem the most probable locations for each model landmark are found in the query volume. On a set of 8 hand CTs we show that this approach is able to consistently localize the model landmarks (finger tips, joints, etc) despite the complex and repetitive structure of the object.
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Detection of 3D Spinal Geometry Using Iterated Marginal Space Learningelects the most likely candidates using a learned prior on the individual nine dimensional transformation spaces. Since the proposed approach is learning-based it can be trained for MR or CT alike. Experimental results based on 42 MR volumes show that our system not only achieves superior accuracy b
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Regression Forests for Efficient Anatomy Detection and Localization in CT Studiese CT scans. Localization errors are shown to be lower (and more stable) than those from global affine registration approaches. The regressor’s parallelism and the simplicity of its context-rich visual features yield typical runtimes of only 1.. Applications include semantic visual navigation, image
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Simultaneous Multi-object Segmentation Using Local Robust Statistics and Contour Interactionte these drawbacks as follows. First, local robust statistics are used to describe the object features, which are learned adaptively from user provided seeds. Second, several active contours evolve simultaneously with their interactions being governed by simple principles derived from mechanics. Thi
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René Donner,Erich Birngruber,Helmut Steiner,Horst Bischof,Georg Langs
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B. Michael Kelm,S. Kevin Zhou,Michael Suehling,Yefeng Zheng,Michael Wels,Dorin Comaniciu
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Antonio Criminisi,Jamie Shotton,Duncan Robertson,Ender Konukoglu
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