Brain Imaging Research Group Occasional Seminar
Time: 11:00am, Friday, Nov. 11, 2005
Place: SCILS Building, College Avenue Campus. Room 323
Speaker: Vasileios Megalooikonomou from Temple University
Title: Detecting Patterns in Brain Images
Abstract:
Understanding patterns and discovering associations, regularities and anomalies between anatomical structures and normal or abnormal function of the human brain is a fundamental goal in the neuroscience community. Current advances in brain image acquisition techniques have made available enormous amounts of remarkable high-resolution three-dimensional (3-D) image data. In addition to the continuous development of improved brain imaging techniques, greater computer capabilities and improvements in normalization techniques are leading to the creation of large databases of structure/function information. The availability of this data has already facilitated many advances in human brain mapping during the last decade. The analysis and exploitation of such large collections of brain image data though still remains a problem. The field of data mining in brain imaging addresses the question of how best to use this data to gain a deeper understanding of how the brain functions improving, as a result, the process of medical decision-making.
Major issues in the current attempts for managing this data are the efficiency, effectiveness and robustness of the database and data mining tools used to extract knowledge (in the form of patterns, associations, etc). In this talk we discuss ways to overcome these problems and address the great need for developing efficient brain data mining tools for the analysis and management of large collections of brain images (from various imaging modalities) and associated clinical data. We propose a general unified framework for managing spatial regions of interest (ROIs) in brain images. We present methods for content-based (similarity) retrieval, characterization, analysis, and classification of spatial region data. Furthermore, we introduce approaches for mining associations between spatial distributions of ROIs and other clinical assessment. We demonstrate the use of the proposed mining methods by applying them to epidemiological data finding clinically meaningful associations. These informatics tools show potential for advancing our abilities to analyze brain image data and facilitating the understanding of patterns in brain structure and function.
Biographical Note:
Vasileios Megalooikonomou received his B.E. in Computer Engineering and Informatics from the University of Patras, Greece, in 1991, and his M.S. and Ph.D. in Computer Science from the University of Maryland, Baltimore County in 1995 and 1997, respectively. He is currently an Associate Professor of Computer and Information Sciences and Director of the Data Engineering Laboratory (denlab.temple.edu) at Temple University. Prior to joining Temple University he was on the faculty (Visiting Assistant Professor) of the Department of Computer Science at Dartmouth College (1999-2000) and held a postdoctoral research associate position at Johns Hopkins University (1997-1999). Dr. Megalooikonomou has received a CAREER Award by the National Science Foundation (2003). He has published over 50 refereed articles in journals and conference proceedings. His research interests include biomedical informatics, data mining, data compression, multimedia database systems, and pattern recognition. His current research is funded by the National Science Foundation and the National Institutes of Health.
Email: vasilis@temple.edu. Web page: http://knight.cis.temple.edu/~vasilis.