Novel Methods in Medical Image Computing with Applications to Tumor Growth Models: Dr. Tannenbaum, Comprehensive Cancer Institute, NIH

1:00 pm on Monday, January 21, 2013
2:00 pm on Monday, January 21, 2013
MCS 148
If you cannot get into the building, please call 973 508 0126. Talk Topic: Medical Imaging Speaker Bio: Dr.Tannenbaum received his Ph.D. in mathematics from Harvard in 1976. He has held faculty positions at several universities in the US, Israel, and Canada. He is presently the Goodrich Professor at the Comprehensive Cancer Institute at UAB and researcher at NIH. Dr. Tannenbaum has authored or co-authored about 470 research papers, and is the author of four books.. He also has four patents in computer vision and medical imaging. Dr.~Tannenbaum has been an Associate Editor of a number of journals including SIAM J. Control and Optimization, Systems and Control Letters, Robust and Nonlinear Control, SIAM Journal Imaging Science. He has won several awards including the Kennedy Research Prize, George Taylor Research Award, IEEE Fellow, SICE Best Paper Award, Foams 2000 Best Paper Award, MICCAI Best Paper Award, and Hugo Schuck Award (Best Paper at ACC). He has given a number of plenary talks including at SIAM, IEEE CDC 2000, MTNS, and SCICADE. He has done research in medical imaging, image processing, computer vision, robust control, systems theory, robotics, semiconductor process control, operator theory, functional analysis, cryptography, algebraic geometry, and invariant theory. Talk Title and Abstract: Novel Methods in Medical Image Computing with Applications to Tumor Growth Models In this talk, we will describe some novel approaches to medical image computing, including segmentation and registration. Segmentation is the process of extracting key features from imagery. We will describe statistical methods for doing this, especially the extraction of various tumor types from a number of modalities including MRI and CT. This will also include new methods for white matter tractography. Very importantly, we will describe some ideas from feedback control that may be used to close the loop around and robustify both open-loop segmentation and registration algorithms in computer vision. In addition to segmentation, the second key component is registration. Registration is the process of establishing a common geometric reference frame between two or more data sets obtained by possibly different imaging modalities. The registration problem (especially in the deformable case) is still one of the great challenges in vision and medical image processing. Registration has a substantial literature devoted to it, with numerous approaches ranging from optical flow to computational fluid dynamics. For this purpose, we propose using ideas from optimal mass transport. We will show how the information gleaned from this may be used to drive certain tumor growth models. We will demonstrate our techniques on a wide variety of data sets from various medical imaging modalities. The talk is designed to be accessible to a broad audience of computer scientists, medical researchers, clinicians, and engineers. Additional Resources: