Titlebook: A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis; Heike Hufnagel Book 2011 Vieweg+Teubner Verlag | Sprin

美麗動(dòng)人

書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis影響因子(影響力)




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis影響因子(影響力)學(xué)科排名




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis網(wǎng)絡(luò)公開(kāi)度




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis被引頻次




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis被引頻次學(xué)科排名




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis年度引用




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis年度引用學(xué)科排名




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis讀者反饋




書目名稱A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis讀者反饋學(xué)科排名

Cocker

978-3-8348-1722-8Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2011

SEED

A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis978-3-8348-8600-2Series ISSN 2625-9354 Series E-ISSN 2625-9362

Connotation

granite

整理

institute

ARCHE

https://doi.org/10.1007/978-3-642-99970-3 in the literature and to gather knowledge about its behaviour and characteristics for different types of shapes. In section 4.1, the performance measures which are commonly used to assess the quality of SSMs are presented and discussed, and several distance metrics that are suited for point-based S

彎彎曲曲

Differential and Difference Inequalities,ue inhomogeneities, contour gaps, and noise the automatic segmentation of many anatomical structures remains a challenge. Low-level algorithms as region growing, thresholding or simple edge-detection are often bound to fail or require heavy user interaction to lead to acceptable segmentation results

Palpate

Differential and Difference Inequalities,the literature. The emphasis of this thesis however was not so much to propose a convenient SSM to solve a specific practical problem but to investigate the possibilities of a novel approach to SSM computation. The focus of this manuscript is twofold: First, a novel SSM method was developed in a pro