The signal and image processing research area faculty are a diverse group of researchers dedicated to technological advances in signal and image processing, with a primary focus on biomedical application. The activities include development of new methods of image acquisition, image processing and analysis, machine learning, and biological system kinetic modeling. Within the group, efforts are made toward understanding the progression of kidney disease, automated recognition of animal vocalizations, the building of medical imaging devices, algorithms, and software methods for MRI, phase-contrast x-ray, mammography, CT, PET, SPECT, and fluorescence imaging to address diseases such as Alzheimer's disease, heart disease, and various forms of cancer.
Faculty members conducting research in the signal and image processing area include:
Associate Professor of Electrical and Computer Engineering; Associate Professor of Biomedical Engineering
Expertise: Medical image quality assessment based on a human-observer model using machine learning, analyzer-based phase contrast x-ray imaging, 4-D and 5-D tomographic image reconstruction for cardiac SPECT, and quantitative molecular imaging using dual-tracer kinetic modeling. He established the Advanced Xray Imaging Laboratory (AXIL) at IIT, which is currently developing a phase-sensitive x-ray device.
Associate Professor of Electrical and Computer Engineering
Expertise: Computational intelligence and machine learning in medical imaging, computer-aided detection and diagnosis, and medical image processing and analysis. He is inventor on 30 patents (including 11 granted patents), which were licensed to several companies and commercialized.
Motorola Professor of Electrical and Computer Engineering; Director of Medical Imaging Research Center (MIRC); Professor of Biomedical Engineering
Expertise: Low-dose SPECT imaging based on 4-D reconstruction and personalized imaging, crime prediction and analysis, analyzer-based x-ray imaging, imaging biomarkers based on machine learning for Alzheimer's disease and dementia, and other applications of machine learning in medicine.
Associate Dean for Analytics for Armour College of Engineering; Professor of Electrical and Computer Engineering
Expertise: Signal processing and control systems; system identification and parameter estimation, including adaptive filtering, with applications in signal processing, control, and biomedical settings; modeling vascular dynamics in the kidney to help in understanding the progression of kidney disease, developing methodologies for the automated recognition of animal vocalizations, and analyzing the dynamics of control of power electronic systems; development and analysis of algorithms and model structures for effective system identification and adaptive filtering, using both linear and nonlinear model structures.
Harris Perlstein Professor of Electrical and Computer Engineering; Professor of Biomedical Engineering
Expertise: Signal and image processing, medical imaging, computer vision, and applied mathematical methods.