Modern multi-dimensional imaging in radiology yields much more information than the naked eye can appreciate. As a result, errors and variations in interpretations are currently the weakest aspect of clinical imaging. Computerized image analysis may provide solutions for ensuring the quality of medical image interpretation by yielding exhaustive, comprehensive and reproducible analysis of imaging features that are difficult to recognize with the naked eye.
We develop computational models of multi-dimensional morphological properties of biomedical tissue. Riesz wavelets and machine learning algorithms are used to learn the organization of image scales and directions that is specific to a given biomedical tissue type. The models obtained can be “steered” analytically to enable rotation-covariant image analysis. While most rotation-invariant approaches discard precious information about image directions, rotation-covariant analysis enables modeling the local organization of image directions independently from their global orientation. Although already being important in 2D images, this becomes crucial to adequately leverage directional image information in 3D images.
Research interests
- Build digital models of disease-specific radiological phenotypes at the organ level using localization systems and texture-based computational models of biomedical tissue
- N-dimensional texture analysis with control of scales and orientations
- Personalized medicine via image-based tissue analysis and machine learning for cancer and other diseases (Radiomics)
- Clinical workflows of image-based computer-aided systems