NDLI: Model-driven multicomponent volume exploration

Content Provider	Springer Nature Link
Author	Shen, Enya Xia, Jiazhi Cheng, Zhiquan Martin, Ralph R. Wang, Yunhai Li, Sikun
Copyright Year	2014
Abstract	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.
Starting Page	441
Ending Page	454
Page Count	14
File Format	PDF
ISSN	01782789
Journal	The Visual Computer
Volume Number	31
Issue Number	4
e-ISSN	14322315
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-04-29
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Interactive volume visualization Volume segmentation Volume correspondence Knowledge-assisted visualization Computer Graphics Computer Science Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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