Computational Methods for Medical Image Interpretation

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Digital image data is being generated and collected at an astounding rate for numerous applications such as social media communication, defence and security, industrial manufacturing, agriculture and environment, science and biology, healthcare and medicine, etc. Today, static and dynamic images (video) constitute about 80% of all big data. This big `visual' data hides within it extraordinary amount of information that is critical for providing new insights, building predictive models, guiding decision making, performing search and curation, etc. This is necessitating the design of new computational tools for image analysis that no longer is possible to carry out manually via visual inspection.******The objective of the proposed research is to develop computer methods that address the key challenges towards automated, accurate, robust, and fast image analysis. Specifically, I will develop novel mathematical models and computational techniques for the following fundamental image interpretation tasks, which are necessary for harnessing visual information from raw image data: (i) image segmentation, to partition images into meaningful parts for subsequent quantification and decision making; (ii) image registration, for pair/group-wise image alignment enabling comparative studies and constructing probabilistic models of objects; and (iii) image classification, for discovering discriminatory visual patterns and features for assigning quantitative values or categorical class labels to visual data. More technically, I will focus on optimization-based formulations to solving the above three tasks. I will develop methods to construct the underlying objective functions by combining domain expert knowledge with machine learning techniques (from training databases). I will address the optimizabilty-fidelity tradeoff in these formulations by developing new representations for the unknowns (e.g. object shape geometry) that facilitate efficient optimization and inference.******The complexity and variety of biomedical image data, the challenges facing their automated analysis, and the opportunities they provide for advancing healthcare, make them an ideal application domain for the proposed research. The image interpretation results will be invaluable for supporting diagnostics and therapeutics in many clinical applications. However, two applications will be emphasized given their societal and economic burden: oncology (e.g. organ and tumour delineation for radiation therapy) and neurology (e.g. discovering imaging biomarkers for neuro-development and neuro-degeneration).**