Titlebook: Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging; Second International Bjoern H. Menze,Georg Langs,Anton

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书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging影响因子(影响力)




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging影响因子(影响力)学科排名




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging网络公开度




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging网络公开度学科排名




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging被引频次




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging被引频次学科排名




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging年度引用




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging年度引用学科排名




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging读者反馈




书目名称Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging读者反馈学科排名

Communal

Groupwise Spectral Log-Demons Framework for Atlas Constructionregistrations by extending the symmetric Log-Demons algorithm. We describe and evaluate two forms of our framework: the . (GL-Demons) is faster but is limited to local nonrigid deformations, and the . (GSL-Demons) is slower but, due to isometry-invariant representations of images, can construct atla

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Carotid Artery Wall Segmentation by Coupled Surface Graph Cutsctions that highlight both inner and outer vessel wall borders, the method combines the search for both borders into a single graph cut optimization. Our approach requires little user interaction and can robustly segment the carotid artery bifurcation. Experiments on 32 carotid arteries from 16 pati

CHART

Graph Cut Segmentation Using a Constrained Statistical Model with Non-linear and Sparse Shape Optimiv Random Field (MRF) segmentation framework. The employed SM based on Probabilistic Principal Component Analysis (PPCA) allows to compute local information about the remaining variance . uncertainty about the correct segmentation boundary. This knowledge about the local segmentation uncertainty is t

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Novel Vector-Valued Approach to Automatic Brain Tissue Classificationcriminant Analysis (KFDA). In Computer Vision, KFDA has been shown to be competitive with other state-of-the-art techniques. In the KFDA-based framework, we exploit the complex structure of grey matter, white matter and cerebro-spinal fluid intensity clusters to find an optimal classification. We il

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Interlocking

Spatially Aware Patch-Based Segmentation (SAPS): An Alternative Patch-Based Segmentation Framework as a .-nearest neighbour problem as the labelling of each voxel is determined according to the distances to its most similar patches. However, the reliance on a good affine registration given the use of limited search windows is a potential weakness. This paper presents a novel alternative framewor