Computational optimization techniques for medical image analysis

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I propose to develop highly-automated, robust and accurate computational techniques for medical image analysis (MIA). Novel image processing, data analysis, artificial intelligence, and artificial life techniques will be investigated and validated in a medical context.          Medical imaging provides challenging algorithmic problems and has a potential for improving health. The challenges are primarily due to the large anatomical shape variability (e.g. humeral bicipital groove), non-rigid anatomy (e.g. myocardium), noisy medical images (e.g. MRI, CT, or SPECT), data variety (e.g. 2D colour microscopy, 3D MRI, time-varying PET, or diffusion tensor (DT) MRI), and higher demand for robustness and accuracy in medicine compared to other areas.          Optimization problems are often encountered in MIA. My proposed research involves: 1) investigating the limitations and strengths of existing optimization techniques for understanding the visual data in medical images; 2) formulating MIA problems in new ways amenable to intuitive encoding of heuristics and domain knowledge; and 3) extending optimization techniques to obtain improved convergence and optimality. I will focus on the problems of: (i) medical image segmentation (identifying the boundaries of structures and organs in images, e.g. for subsequent quantification); (ii) medical image registration (spatial alignment of images, e.g. for building variational atlases or fusing multi-modal images); and (iii) structural shape correspondence (pairing homologous points on shapes, e.g. for shape classification and statistical analysis). For segmentation, I will extend scalar image segmentation algorithms to DTMRI data and propose alternative knowledge-driven optimization formulations. For image registration, I will optimize registration strategies rather than using low-level metrics as objective functions. I will also develop alternative optimization approaches for shape correspondence and propose a unified framework for shape correspondence and image registration.