caa a22 4500 605540721 CHVBK 20210128100912.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00371-014-0940-7 doi (NATIONALLICENCE)springer-10.1007/s00371-014-0940-7 Model-driven multicomponent volume exploration [Elektronische Daten] [Enya Shen, Jiazhi Xia, Zhiquan Cheng, Ralph Martin, Yunhai Wang, Sikun Li] The current multicomponent volume segmentation and labeling methods are mostly hard to get correct segmentation and labeling results automatically and rely hardly on experts' aids, which make related volume exploration to be time consuming, laborious and prone to errors and omissions. To solve this problem, we present a novel volume exploration method driven by admitted model. We first apply Gaussian mixture models to segment the raw volume. However, different components with similar value are still mixed. To segment these components further, we make use of region-grown principle to produce a fine-grained part segmentation. To label different parts automatically, we found that it is helpful to take advantage of annotated model, like human anatomy model (PlasticboyCC, http://www.plasticboy.co.uk/store/index.html , 2013). However, it is not straightforward to label segmented volume with geometric model automatically. Inspired by electors voting (Au et al., Comput Graph Forum 29:645-654, 2010), we propose a volume-model correspondence schema to overcome this intractable challenge. Moreover, it is essential to exploit intuitive interactive methods for interactive exploration, so we also developed practical precise interaction techniques to assist volume exploration. Our experiments with various data and discussion with specialists show that our method provides an efficient and impactful way to explore volume data. Springer-Verlag Berlin Heidelberg, 2014 Interactive volume visualization nationallicence Volume segmentation nationallicence Volume correspondence nationallicence Knowledge-assisted visualization nationallicence Shen Enya School of Computer, National University of Defense Technology, Changsha, Hunan, China aut Xia Jiazhi School of Information Science and Engineering, Central South University, Changsha, Hunan, China aut Cheng Zhiquan School of Computer, National University of Defense Technology, Changsha, Hunan, China aut Martin Ralph School of Computer Science and Informatics, Cardiff University, Cardiff, Wales, UK aut Wang Yunhai Shenzhen VisuCA Key Lab/SIAT, Shenzhen, China aut Li Sikun School of Computer, National University of Defense Technology, Changsha, Hunan, China aut The Visual Computer Springer Berlin Heidelberg 31/4(2015-04-01), 441-454 0178-2789 31:4<441 2015 31 371 https://doi.org/10.1007/s00371-014-0940-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-0940-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Shen Enya School of Computer, National University of Defense Technology, Changsha, Hunan, China aut NATIONALLICENCE 700 1- Xia Jiazhi School of Information Science and Engineering, Central South University, Changsha, Hunan, China aut NATIONALLICENCE 700 1- Cheng Zhiquan School of Computer, National University of Defense Technology, Changsha, Hunan, China aut NATIONALLICENCE 700 1- Martin Ralph School of Computer Science and Informatics, Cardiff University, Cardiff, Wales, UK aut NATIONALLICENCE 700 1- Wang Yunhai Shenzhen VisuCA Key Lab/SIAT, Shenzhen, China aut NATIONALLICENCE 700 1- Li Sikun School of Computer, National University of Defense Technology, Changsha, Hunan, China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/4(2015-04-01), 441-454 0178-2789 31:4<441 2015 31 371