Titlebook: Machine Learning in Medical Imaging; Second International Kenji Suzuki,Fei Wang,Pingkun Yan Conference proceedings 2011 Springer-Verlag Gmb

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书目名称Machine Learning in Medical Imaging影响因子(影响力)




书目名称Machine Learning in Medical Imaging影响因子(影响力)学科排名




书目名称Machine Learning in Medical Imaging网络公开度




书目名称Machine Learning in Medical Imaging网络公开度学科排名




书目名称Machine Learning in Medical Imaging被引频次




书目名称Machine Learning in Medical Imaging被引频次学科排名




书目名称Machine Learning in Medical Imaging年度引用




书目名称Machine Learning in Medical Imaging年度引用学科排名




书目名称Machine Learning in Medical Imaging读者反馈




书目名称Machine Learning in Medical Imaging读者反馈学科排名

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B-cell

Conference proceedings 2011tion with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical

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A Locally Deformable Statistical Shape Model,o not need predefined segments. Smoothness constraints ensure that the local solution is restricted to the space of feasible shapes. Very promising results are obtained when we compare our new approach to a global fitting approach.

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Segmentation of Skull Base Tumors from MRI Using a Hybrid Support Vector Machine-Based Method,ere used to train a binary SVC (BSVC). By the trained BSVC, the final tumor lesion was segmented out. This method was tested on 13 MR images data sets. Quantitative results suggested that the developed method achieved significantly higher segmentation accuracy than OSVC and BSVC.

granite

Automatic Segmentation of Vertebrae from Radiographs: A Sample-Driven Active Shape Model Approach,ained by a conditional shape model, based on the variability of the coarse spine location estimates. The technique is evaluated on a data set of manually annotated lumbar radiographs. The results compare favorably to the previous work in automatic vertebra segmentation, in terms of both segmentation accuracy and failure rate.

acrophobia

Computer-Assisted Intramedullary Nailing Using Real-Time Bone Detection in 2D Ultrasound Images,alidation of the method has been done using US images of anterior femoral condyles from 9 healthy volunteers. To calculate the accuracy of the method, we compared our results to a manual segmentation performed by an expert. The Misclassification Error (ME) is between 0.10% and 0.26% and the average computation time was 0.10 second per image.

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