Titlebook: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008; 11th International C Dimitris Metaxas,Leon Axel,Gábor Székely Con

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書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008影響因子(影響力)




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008影響因子(影響力)學(xué)科排名




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008網(wǎng)絡(luò)公開度




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008被引頻次




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008被引頻次學(xué)科排名




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008年度引用




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008年度引用學(xué)科排名




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008讀者反饋




書目名稱Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008讀者反饋學(xué)科排名

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On Computing the Underlying Fiber Directions from the Diffusion Orientation Distribution Function represent the ODF as a symmetric high-order Cartesian tensor restricted to the unit sphere and show that the extrema of the ODF are solutions to a system of polynomial equations whose coefficients are polynomial functions of the tensor elements. In addition to demonstrating the ability of our metho

Scleroderma

Extracting Tractosemas from a Displacement Probability Field for Tractography in DW-MRIlity information. The peaks of tractosemas correspond to directions of distinct fibers, which can have either symmetric or asymmetric local fiber structure. This is in contrast to the existing methods that estimate fiber orientation distributions which are naturally symmetric and therefore cannot mo

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Semi-supervised Nasopharyngeal Carcinoma Lesion Extraction from Magnetic Resonance Images Using Onliised segmentation approach to segment the lesion in two steps. First, a metric is learned in a supervised fashion, which maximizes the separation between two groups of pixels (tumor or non-tumor) with minimal user interaction. Second, the learned metric is used to complete extraction of tumor region

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