Medical imaging • Computer vision • Machine learning • Radiomics • Imaging genetics
The overarching objective of my research is to enable computers to extract clinically useful information from medical images to improve our understanding, prevention, diagnosis, and treatment of diseases.
I work on developing computational techniques for solving real-world clinical problems through the automated processing and analysis of multi-dimensional biomedical structural and functional images. Medical images include magnetic resonance imaging (MRI, f-MRI, d-MRI) and computed tomography (X ray-CT, PET, SPECT), microscopy, and ultrasound.
I am interested in the application of machine learning, optimization, and graph theory to medical imaging and clinical applications.
My research focuses on developing techniques for: segmentation and registration, tracking and matching, shape representation and deformation analysis of anatomical structures and functional regions in medical images. I also work on building statistical, physical, and geometrical models of shape variation and on their application to automated detection of structural abnormality and pathology. I am also researching the development of controlled shape deformation techniques, and the incorporation of context-based, artificially intelligent mechanisms for image registration and segmentation using artificial life models.