Muhammad Razib

Florida International University

Lecture Information:
  • June 9, 2016
  • 10:30 AM
  • ECS: 243

Speaker Bio

Muhammad Razib is a Ph.D. candidate in the School of Computing and Information Sciences at FIU. He received his Bachelor’s degree in Computer Science and Engineering in 2009 from Bangladesh University of Engineering and Technology (BUET). Muhammad joined FIU in Fall 2013, under the supervision of Dr. Wei Zeng. His research interests are in the areas of Medical Imaging, Computer Vision and Computer Graphics. His current research focuses on the visualization and geometric analysis of the complex brain structures.

Description

Brain morphometry analysis refers to the study of the size and shape of brain structures and functions and its relations to the development and evolution of the brain due to aging, learning, diseases etc. Analysis techniques typically involve brain mapping and registration. Brain mapping maps the 3D convoluted brain surface onto 2D domain for straightforward visualization and comparison between brain structures. Brain registration is the process of generating accurate correspondence between brains for finding out the similarities and differences in brain structures or functions. There have been lots of researches for developing computational algorithms to automate the process of brain morphometry analysis using brain mapping and registration from structural MRI. But due to the complex structure of the brain surface, generating well-structured view and accurate registration between brains still remain challenging problems.

In this proposal, we aim to develop computational algorithms for generating high quality brain mapping and registration by using anatomical brain network structure. First, we identify the factors that affect the quality of the brain mappings and propose solution to improve the quality, and then propose to use this mapping to register brains and improve the quality by combining the factors that affect the registration. Finally, we will explore brain classification and analysis applications for large-scale brain databases.