Welcome to the Wismüller Lab
The mission of Professor Wismüller's research group is to develop novel intuitively intelligible computational visualization methods for the exploratory analysis of high-dimensional data from biomedical imaging. Specifically, the focus of our research is on developing robust and adaptive systems for computer-aided analysis and visualization which combine principles and computational strategies inspired by biology with machine learning and image processing/computer vision approaches from electrical engineering and computer science.
Research efforts in Professor Wismüller's group are taking place at two complementary levels:
- Mathematical algorithms for computational image analysis
- Pattern recognition in clinical real-world applications
Application areas range from functional MRI for human brain mapping, MRI mammography for breast cancer diagnosis, image segmentation in Multiple Sclerosis and Alzheimer's Dementia to multi-modality fusion, biomedical time-series analysis, and quantitative bio-imaging. Professor Wismüller's laboratory is located in the Rochester Center for Brain Imaging, which houses a whole body 3T Siemens MRI Scanner and several high field magnets.
Current Research Projects
Anas Zainul Abidin receives an Honorable Mention for his poster presentation at SPIE Medical Imaging 2015
Five scientific publications accepted at SPIE Medical Imaging 2015 Conference: Computational Radiology Lab goes to Orlando, Florida
- Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography.PLoS One. 10, e0117157. (2015 Jan 01).
- Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression.J Electron Imaging. 23, 013013. (2014 Feb 04).
- Computer-aided diagnosis for phase-contrast X-ray computed tomography: quantitative characterization of human patellar cartilage with high-dimensional geometric features.J Digit Imaging. 27, 98-107. (2014 Feb 01).