Titlebook: Information Processing in Medical Imaging; 13th International C Harrison H. Barrett,A. F. Gmitro Conference proceedings 1993 Springer-Verla

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书目名称Information Processing in Medical Imaging影响因子(影响力)




书目名称Information Processing in Medical Imaging影响因子(影响力)学科排名




书目名称Information Processing in Medical Imaging网络公开度




书目名称Information Processing in Medical Imaging网络公开度学科排名




书目名称Information Processing in Medical Imaging被引频次




书目名称Information Processing in Medical Imaging被引频次学科排名




书目名称Information Processing in Medical Imaging年度引用




书目名称Information Processing in Medical Imaging年度引用学科排名




书目名称Information Processing in Medical Imaging读者反馈




书目名称Information Processing in Medical Imaging读者反馈学科排名

Narrative

Tincture

The use of active shape models for locating structures in medical images, how the models can be used in local image search and give examples of their application to medical images. We also describe how the method can be simply extended to segment 3-D objects in volume images and to track structures in image sequences.

floaters

mendacity

Aromatic

偶像

Spatial knowledge representation for visualization of human anatomy and function,nowledge, as well as for presenting knowledge of different domains simultaneously. The basic ideas and a pilot implementation for the human skull and brain are presented. Remaining problems and possibilities for future extensions and improvements are discussed.

Mammal

A strategy for automated multimodality image registration incorporating anatomical knowledge and imm three patients using one training set. In each case, the accuracy of registration was comparable to that obtained by point landmark registration. We present initial results for the modified distance transform in the same clinical application, and in a new application to combine angiographic data with the surface of the brain derived from MR.

烤架

Segmentation of Magnetic resonance brain images using analog constraint satisfaction neural networkto each pixel. An improved version of this novel segmentation network that is provably stable is described. Results of the network are presented along with a comparison of these results to a collection of human segmentations. The network is discussed in relation to other methods for segmentation and the network‘s extendibility is described.

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