Gurman Gill - Research

Research Overview

I am broadly interested in learning visual phenomenon from images by designing features and employing classification techniques. My overarching goal is to involve students to employ image analysis and learning for tasks originating in different STEM fields. Towards that goal, I am currently working on projects involving Computed Tomography (CT) scans of human lungs, microscopic images of geological rocks (in collaboration with Dr. Matty Mookerjee, Department of Geology) and digital images of animals in the wild (in collaboration with Dr. Chris Halle at the Center for Environmental Inquiry). More information regarding these projects can be found here. SSU students who are interested in these projects are welcome to contact me!

Publication List

  • G. Gill and R.R. Beichel, An approach for reducing the error rate in automated lung segmentation, Computers in Biology and Medicine, vol. 76, pages 143-153, Sep. 2016. (PDF link)
  • G. Gill and R. R. Beichel, Lung Segmentation in 4D CT Volumes based on Robust Active Shape Model Matching, International Journal of Biomedical Imaging, vol. 3015, Article ID 125648, 9 pages, Sep. 2015. (PDF link)
  • G. Gill and R. R. Beichel, Segmentation of Lungs with Interstitial Lung Disease in CT Scans: A TV-L1 Based Texture Analysis Approach, Advances in Visual Computing, LNCS 8887, pp. 511-520, 2014. (PDF link)
  • G. Gill, M. Toews and R. R. Beichel, Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach, International Journal of Biomedical Imaging, vol. 2014, Article ID 479154, 7 pages, 2014. doi:10.1155/2014/479154. (PDF link)
  • G. Gill, C. Bauer and R. R. Beichel, A Method for Avoiding Overlap of Left and Right Lungs in Shape Model Guided Segmentation of Lungs in CT Volumes, Medical Physics, Vol. 41, 101908, 2014, doi: 10.1118/1.4894817. (PDF link)
  • G. Gill, M. Toews and R. R. Beichel, An Automated Initialization System for Robust Model-Based Segmentation of Lungs in CT Data, 5th International Workshop on Pulmonary Image Analysis, pp. 111-122, 2013.
  • G. Gill and M. Levine, Multi-View Object Detection based on Spatial Consistency in a Low Dimensional Space, German Association for Pattern Recognition, LNCS 5748, pp. 211-220, 2009. (PDF link)
  • G. Gill and M. Levine, Incorporating Shape Features in an Appearance-Based Object Detection System, Computer Analysis of Images and Patterns, LNCS 5702, pp. 269-276, 2009. (PDF link)
  • G. Gill and M. Levine, A Single Classifier for View-Invariant Multiple Object Class Recognition, British Machine Vision Conference, volume 1, pages 257-266, 2006. (PDF link)

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