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Cluster-based co-saliency detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

Fu H, Cao X, Tu Z.
PMID: 23629857
IEEE Trans Image Process. 2013 Oct;22(10):3766-78. doi: 10.1109/TIP.2013.2260166. Epub 2013 Apr 25.

Co-saliency is used to discover the common saliency on the multiple images, which is a relatively underexplored area. In this paper, we introduce a new cluster-based algorithm for co-saliency detection. Global correspondence between the multiple images is implicitly learned...

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Fu H, Cao X, Tu Z. Cluster-based co-saliency detection. IEEE Trans Image Process. 2013;22(10):3766-78doi: 10.1109/TIP.2013.2260166.
Fu, H., Cao, X., & Tu, Z. (2013). Cluster-based co-saliency detection. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 22(10), 3766-78. https://doi.org/10.1109/TIP.2013.2260166
Fu, Huazhu, et al. "Cluster-based co-saliency detection." IEEE transactions on image processing : a publication of the IEEE Signal Processing Society vol. 22,10 (2013): 3766-78. doi: https://doi.org/10.1109/TIP.2013.2260166
Fu H, Cao X, Tu Z. Cluster-based co-saliency detection. IEEE Trans Image Process. 2013 Oct;22(10):3766-78. doi: 10.1109/TIP.2013.2260166. Epub 2013 Apr 25. PMID: 23629857; PMCID: PMC3785793.
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ROTATIONAL GRADIENT FIELD FOR INTERPOLATION OF FIBER ORIENTATION DISTRIBUTION IN CONNECTIVITY ANALYSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging

Li J, Shi Y, Toga AW.
PMID: 25383160
Proc IEEE Int Symp Biomed Imaging. 2014;2014:1051-1054. doi: 10.1109/ISBI.2014.6868054.

Advances in diffusion weighted MR imaging have made it possible to non-invasively study the structral connectivity of human brains at high resolution. To model crossing fibers in white matter, a popular choice is the reconstruction of fiber orientation distributions...

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Li J, Shi Y, Toga AW. ROTATIONAL GRADIENT FIELD FOR INTERPOLATION OF FIBER ORIENTATION DISTRIBUTION IN CONNECTIVITY ANALYSIS. Proc IEEE Int Symp Biomed Imaging. 2014;2014:1051-1054doi: 10.1109/ISBI.2014.6868054.
Li, J., Shi, Y., & Toga, A. W. (2014). ROTATIONAL GRADIENT FIELD FOR INTERPOLATION OF FIBER ORIENTATION DISTRIBUTION IN CONNECTIVITY ANALYSIS. Proceedings. IEEE International Symposium on Biomedical Imaging, 20141051-1054. https://doi.org/10.1109/ISBI.2014.6868054
Li, Junning, et al. "ROTATIONAL GRADIENT FIELD FOR INTERPOLATION OF FIBER ORIENTATION DISTRIBUTION IN CONNECTIVITY ANALYSIS." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2014 (2014): 1051-1054. doi: https://doi.org/10.1109/ISBI.2014.6868054
Li J, Shi Y, Toga AW. ROTATIONAL GRADIENT FIELD FOR INTERPOLATION OF FIBER ORIENTATION DISTRIBUTION IN CONNECTIVITY ANALYSIS. Proc IEEE Int Symp Biomed Imaging. 2014;2014:1051-1054. doi: 10.1109/ISBI.2014.6868054. PMID: 25383160; PMCID: PMC4224307.
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Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain.

IEEE signal processing magazine

Li J, Shi Y, Toga AW.
PMID: 27212872
IEEE Signal Process Mag. 2016 May;33(3):36-51. doi: 10.1109/MSP.2015.2510024.

No abstract available.

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Li J, Shi Y, Toga AW. Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain. IEEE Signal Process Mag. 2016;33(3):36-51doi: 10.1109/MSP.2015.2510024.
Li, J., Shi, Y., & Toga, A. W. (2016). Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain. IEEE signal processing magazine, 33(3), 36-51. https://doi.org/10.1109/MSP.2015.2510024
Li, Junning, et al. "Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain." IEEE signal processing magazine vol. 33,3 (2016): 36-51. doi: https://doi.org/10.1109/MSP.2015.2510024
Li J, Shi Y, Toga AW. Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain. IEEE Signal Process Mag. 2016 May;33(3):36-51. doi: 10.1109/MSP.2015.2510024. PMID: 27212872; PMCID: PMC4869891.
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FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE.

Proceedings. IEEE International Symposium on Biomedical Imaging

Prasad G, Joshi SH, Nir TM, Toga AW, Thompson PM.
PMID: 25067993
Proc IEEE Int Symp Biomed Imaging. 2013;2013:258-261. doi: 10.1109/ISBI.2013.6556461.

We present a new flow-based method for modeling brain structural connectivity. The method uses a modified maximum-flow algorithm that is robust to noise in the diffusion data and guided by biologically viable pathways and structure of the brain. A...

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Prasad G, Joshi SH, Nir TM, et al. FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2013;2013:258-261doi: 10.1109/ISBI.2013.6556461.
Prasad, G., Joshi, S. H., Nir, T. M., Toga, A. W., & Thompson, P. M. (2013). FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE. Proceedings. IEEE International Symposium on Biomedical Imaging, 2013258-261. https://doi.org/10.1109/ISBI.2013.6556461
Prasad, Gautam, et al. "FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE." Proceedings. IEEE International Symposium on Biomedical Imaging vol. 2013 (2013): 258-261. doi: https://doi.org/10.1109/ISBI.2013.6556461
Prasad G, Joshi SH, Nir TM, Toga AW, Thompson PM. FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2013;2013:258-261. doi: 10.1109/ISBI.2013.6556461. PMID: 25067993; PMCID: PMC4109645.
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Locally Weighted Multi-atlas Construction.

Multimodal brain image analysis : third International Workshop, MBIA 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings. MBIA (Workshop) (3rd : 2013 : Nagoya-shi, Japan)

Li J, Shi Y, Dinov ID, Toga AW.
PMID: 25392851
Multimodal Brain Image Anal (2013). 2013 Jan 01;8159:1-8. doi: 10.1007/978-3-319-02126-3_1.

In image-based medical research, atlases are widely used in many tasks, for example, spatial normalization and segmentation. If atlases are regarded as representative patterns for a population of images, then multiple atlases are required for a heterogeneous population. In...

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Li J, Shi Y, Dinov ID, et al. Locally Weighted Multi-atlas Construction. Multimodal Brain Image Anal (2013). 2013;8159:1-8doi: 10.1007/978-3-319-02126-3_1.
Li, J., Shi, Y., Dinov, I. D., & Toga, A. W. (2013). Locally Weighted Multi-atlas Construction. Multimodal brain image analysis : third International Workshop, MBIA 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings. MBIA (Workshop) (3rd : 2013 : Nagoya-shi, Japan), 81591-8. https://doi.org/10.1007/978-3-319-02126-3_1
Li, Junning, et al. "Locally Weighted Multi-atlas Construction." Multimodal brain image analysis : third International Workshop, MBIA 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings. MBIA (Workshop) (3rd : 2013 : Nagoya-shi, Japan) vol. 8159 (2013): 1-8. doi: https://doi.org/10.1007/978-3-319-02126-3_1
Li J, Shi Y, Dinov ID, Toga AW. Locally Weighted Multi-atlas Construction. Multimodal Brain Image Anal (2013). 2013 Jan 01;8159:1-8. doi: 10.1007/978-3-319-02126-3_1. PMID: 25392851; PMCID: PMC4225708.
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Showing 1 to 5 of 5 entries