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Segmentator v1.4.0

Abstract

{"references": ["Kindlmann, G., & Durkin, J. W. (1998). Semi-automatic generation of transfer functions for direct volume rendering. In Proceedings of the 1998 IEEE symposium on Volume visualization - VVS '98 (pp. 79\u201386). New York, New York, USA: ACM Press. http://doi.org/10.1145/288126.288167", "Kniss, J., Kindlmann, G., & Hansen, C. (2001). Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets. In Proceedings Visualization, 2001. VIS '01. (pp. 255\u2013562). IEEE. http://doi.org/10.1109/VISUAL.2001.964519", "Kniss, J., Kindlmann, G., & Hansen, C. (2002). Multidimensional transfer functions for interactive volume rendering. IEEE Transactions on Visualization and Computer Graphics, 8(3), 270\u2013285. http://doi.org/10.1109/TVCG.2002.1021579", "Kniss, J., Kindlmann, G., & Hansen, C. D. (2005). Multidimensional transfer functions for volume rendering. Visualization Handbook, 189\u2013209. http://doi.org/10.1016/B978-012387582-2/50011-3", "Jianbo Shi, & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888\u2013905. http://doi.org/10.1109/34.868688", "Ip, C. Y., Varshney, A., & Jaja, J. (2012). Hierarchical exploration of volumes using multilevel segmentation of the intensity-gradient histograms. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2355\u20132363. http://doi.org/10.1109/TVCG.2012.231"]}

Segmentator is a free and open-source package for multi-dimensional data exploration and segmentation for 3D images. This application is mainly developed and tested using ultra-high field magnetic resonance imaging (MRI) brain data.

For details, visit https://github.com/ofgulban/segmentator/wiki

Related Organizations
Keywords

Magnetic resonance imaging, segmentation, magnetic resonance imaging, image, MRI

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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