Research & Publications
Dr. Staib's research centers on techniques for accurate analysis, quantification and clinical characterization of medical images including diagnosis, prognosis and treatment decision support. Current medical imaging modalities can reveal rich information about structure and function in three dimensions and in vivo. However, in order to extract measures that are meaningful for scientific or clinical purposes, it is necessary to have quantitative methods of medical image analysis that are robust in the presence of noise, acquisition variation, etc.
Dr. Staib is developing methods for the measurement and characterization of images using machine learning and model-based approaches.
Specialized Terms: Automated biomedical image analysis and measurement; machine learning; deep learning; applications to cancer, neuroscience and cardiology; functional magnetic resonance image (fMRI) analysis; diffusion weighted magnetic resonance (DW-MR) image analysis
Biomedical Engineering; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Radiographic Image Interpretation, Computer-Assisted; Radiology; Neural Networks, Computer; Diffusion Magnetic Resonance Imaging; Machine Learning
- Methods for Nonrigid Image Registration, In: Handbook of Geometric Computing: Applications in Pattern RecognitionL. H. Staib, Y. M. Wang, Methods for Nonrigid Image Registration, In: Handbook of Geometric Computing: Applications in Pattern Recognition, Computer Vision, Neuralcomputing, and Robotics, E. Bayro-Corrochano, editor, Springer-Verlag, pages 571-602, 2005.