Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.
Dr. Shireen Elhabian

Dr. Shireen Elhabian - Research Assistant Professor

WEB 2815
phone (801) 587-3206
fax (801) 585-6513
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Dr. Elhabian received her BSc. and MSc. from Faculty of Computers and Information, Cairo University (FCI-CU), Egypt, in 2002 and 2005, respectively. She received her PhD in Electrical and Computer Engineering (ECE) from University of Louisville (UofL), USA, in Fall 2012. Since Fall 2002, she has joined FCI-CU as a teaching assistant. Since then she has earned professional as well as academic experience with a number of peer reviewed publications. From 2007-2012, she was a graduate research assistant at Computer Vision and Image Processing (CVIP) Lab at UofL. During this time, she was involved in different research projects including (1) pulmonary nodule detection and classification for the early diagnosis of lung cancer, (2) volumetric representation of the corpus callosum for early detection of autism, (3) image-based approach for the reconstruction of plausible human jaw in-vivo for the treatment of malocclusion problems, (4) automated framework for 3D colon segmentation for accurate polyp detection and (5) illumination invariant statistical facial shape recovery for 3D face recognition. Dr. Elhabian also conducted teaching and mentoring undergraduate as well as graduate students in topics related to computer vision, image processing, signal processing and pattern recognition in FCI-CU and CVIP-UofL. Dr. Elhabian has extensive programming experience in many languages and methodologies.

Dr. Elhabian was awarded the best teaching assistant in Cairo University in 2005 and the outstanding ECE graduate student award at the University of Louisville in 2009. Recently, she has been selected, from a worldwide search to be among 30 of the best PhD candidates to participate in Computer Vision and Pattern Recognition (CVPR) Doctoral Consortium in 2012. Further, her PhD thesis received the UofL Graduate Dean's Citation award.

In 2013, Dr. Elhabian joined the Scientific Computing and Imaging Institute, University of Utah, as a post-doctoral research fellow then associate. She worked on the development of efficient algorithms for seismic horizons tracking and the representation of geological areas. She was involved in the development and validation of statistical shape model for different medical applications. She also performed image analysis and processing of diffusion MRI data as a part of neuro-developmental studies.

Current Responsibilities

Dr. Elhabian current research and development is focused on:
  • Developing and validating of statistical shape models for different medical applications, including orthopedics and cardiology, for objectively studying the three-dimensional relationships between form and function of anatomical structures as a part of studying diseases
  • Dense surface reconstruction from sparse point-based correspondence models
  • Investigating potential approaches for constructing 4D shape models from thick-slices cine MRI acquisition
  • Novel generative shape models for nonlinear shape spaces
  • Shape-driven image segmentation
  • Image analysis and processing of diffusion MRI data
  • Non-rigid registration and longitudinal analysis of diffusion MRI

Research Interests

Advances in visual data acquisition devices and computer hardware in recent years have generated large amounts of visual data, adding various exciting research areas related to understanding and leveraging these visual data. Dr. Elhabian research interests have been centered around medical imaging, computer vision, image understanding and pattern recognition. Her primary focus is on statistical shape analysis, subspace learning, generative image and shape modeling, geometric and photometric object representation. She is also fascinated with the implications of advances in these fields to society and industry.