Welcome to the Medical Image Analysis Laboratory!

Our emphasis is on developing and optimizing methods and software for quantitative analysis of structure and function in medical images with particular focus on the study of psychiatric illness. We work with many types of brain imaging data, including functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), electroencephalography (EEG), and structural imaging.

Much of our time is spent working on new methods for flexible analysis of brain imaging data. The use of data driven approaches is very useful for extracting potentially unpredictable patterns within these data. However such methods can be further improved by incorporating additional prior information as constraints, in order to benefit from what we know. To this end, we draw heavily from the areas of image processing, adaptive signal processing, estimation theory, neural networks, statistical signal processing, and pattern recognition.

Front Row (From left to right): Jill Fries, Srinivas Rachakonda, Ravi Kalyanam, Michelle Juarez,

Zhaomei Feng, Andrew Michael, Rogers Silva, Jing Sui, Lai Xu

Back Row (From left to right): Sunil Arja, Unal Sakoglu, Arvind Caprihan, Nathan Swanson,

Dae il Kim, Judith Segall, Vince D. Calhoun

 

Vince D. Calhoun, Ph.D

Director, Image Analysis and MR Research, The MIND Research Network
Associate Professor, Electrical and Computer Engineering, University of New Mexico
Associate Professor, Adjunct, Dept. of Psychiatry, Yale University