Difference between revisions of "John"

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{{h2 | John Bogovic}}
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{{h2|John Bogovic Ph.D.}}
[[Image:JohnHeadshot.jpg|left|thumb|250px|Ph.D Candidate<br>
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[[Image:JohnHeadshot.jpg|right|thumb|250px|Postdoc<br>
 
Image Analysis and Communications Laboratory<br>
 
Image Analysis and Communications Laboratory<br>
 
Department of Electrical Engineering<br>
 
Department of Electrical Engineering<br>
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{{h4 | Bio}}
 
{{h4 | Bio}}
I began my studies in Electrical Engineering at Washington University in St. Louis, where I earned a B.S. in 2006. He has continued my studies at the Johns Hopkins University, earning a MEng in 2008. I am now a member of the Image Analysis and Communications Laboratory at Johns Hopkins and is pursuing a Ph.D. My research interests include diffusion tensor imaging, statistical label fusion, manual delineation, and anatomical studies of the cerebellum focusing on shape variation.
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I began my studies in Electrical Engineering at Washington University in St. Louis, where I earned a B.S. in 2006. I continued my studies at the Johns Hopkins University, earning a MEng in 2008. Currently, I completed a Ph.D in Electrical Engineering as a member of the Image Analysis and Communications Laboratory in February 2013. My research interests include image segmentation, level set methods, machine learning, diffusion tensor imaging, statistical label fusion, manual delineation, and anatomical studies of the cerebellum focusing on shape variation.
*[[JohnCV|Curriculum Vitae]]
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*{{file|Bogovic_cv.pdf|Curriculum Vitae}}
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*[http://www.linkedin.com/pub/john-bogovic/54/95a/980 my LinkedIn page]
 
*[[JohnResearch|Research]]
 
*[[JohnResearch|Research]]
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**[[JohnVisualizations|Data Visualization]]
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{{h4 | Software}}
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{{h5 | Developer}}
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* [http://www.nitrc.org/projects/mgdm/ MGDM] - Multiple-object Geometric Deformable Model
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* [http://www.nitrc.org/projects/jist/ JIST] - the Java Image Science toolkit
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* [http://www.nitrc.org/projects/toads-cruise/ TOADS-CRUISE] - MR brain segmentation and analysis tools
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* [http://www.nitrc.org/plugins/mwiki/index.php/jist:CATNAP CATNAP] - Coregistration, Adjustment, and Tensor-solving - a Nicely Automated Program
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* [http://www.nitrc.org/projects/mrcap MRCAP] - MR Connectome Automated Pipeline (MRCAP)
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{{h5 | Supporter}}
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* [http://www.nitrc.org/projects/dots DOTS] - Diffusion-Oirented Tract Segmentation
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* [http://www.nitrc.org/projects/masi-fusion MASI Fusion] - Statistical and voting label fusion techniques
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{{h4 | Teaching + Tips}}
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* [[MedImgAnalysis2011|Medical Image Analysis 2011]]
 
*[[JohnTools|Some of my favorite tools]]
 
*[[JohnTools|Some of my favorite tools]]
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*[[JohnMatlabTips|Matlab tips and notes]]
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**[http://iacl.ece.jhu.edu/w/images/2/24/Mia2011_matlabTut.pdf A very brief MATLAB tutorial for newcomers]
  
 
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{{h4 | Research}}
*[[IACL:JohnJournal|Research Journal]] (internal use only!)
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*[[User:John|Research Journal]] (internal use only!)

Latest revision as of 00:54, 3 July 2022


John Bogovic Ph.D.

Postdoc
Image Analysis and Communications Laboratory
Department of Electrical Engineering
Johns Hopkins University


Bio

I began my studies in Electrical Engineering at Washington University in St. Louis, where I earned a B.S. in 2006. I continued my studies at the Johns Hopkins University, earning a MEng in 2008. Currently, I completed a Ph.D in Electrical Engineering as a member of the Image Analysis and Communications Laboratory in February 2013. My research interests include image segmentation, level set methods, machine learning, diffusion tensor imaging, statistical label fusion, manual delineation, and anatomical studies of the cerebellum focusing on shape variation.

Software

Developer
  • MGDM - Multiple-object Geometric Deformable Model
  • JIST - the Java Image Science toolkit
  • TOADS-CRUISE - MR brain segmentation and analysis tools
  • CATNAP - Coregistration, Adjustment, and Tensor-solving - a Nicely Automated Program
  • MRCAP - MR Connectome Automated Pipeline (MRCAP)
Supporter
  • DOTS - Diffusion-Oirented Tract Segmentation
  • MASI Fusion - Statistical and voting label fusion techniques

Teaching + Tips

Research