Feature-based methods for 3D medical image analysis

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Medical imaging modalities such as MRI, ultrasound and CT allow us to visualize the 3D human body in-vivo, to make quantitative statements regarding anatomy or to diagnose pathologies or disorders. Collections of medical image data are growing rapidly in size and offer unprecedented opportunities for large-scale analyses to quantify anatomical variability and understand disease processes, for example Alzheimer's disease in brain MRI or chronic obstructive pulmonary disease (COPD) in lung CT images. Given pressures on health care systems, an urgent need exists for robust, scalable computational tools capable of analyzing large quantities of diverse medical image data.The long term goal of my research is to develop state-of-the-art algorithms for analyzing volumetric medical image data, in particular large sets of medical images. The short term goals will target primary computational tasks and clinical applications. The results will include computational tools for large-scale computer-assisted analysis and diagnosis of medical image data, which will help medical health practitioners provided more accurate, evidence-based decisions. Medical image analysis focuses on computational algorithms for addressing clinical research questions pertaining the structure and function of biological organisms from image data. Major research challenges stem from robustly coping with variations in image appearance and geometry, potentially due to abnormalities such as pathology or injury, scaling to large numbers of data in terms of efficiency in computational processing. Primary computational tasks include the following: * Registration: aligning different images of the same underlying object or tissue. * Segmentation: delineating tissues or objects of interest. * Classification and Regression: predicting unknown clinical parameters of interest from image data, such as disease state. * Discovery: identifying image structure correlated with parameters of interest.My research develops a general computational framework for medical image analysis, entitled feature-based analysis (FBA). FBA models medical image data as a collage of generic image patches or features, such as blob- or corner-like structures, that are automatically extracted in a manner invariant to global variations in image geometry (e.g. due to patient positioning in the scanner) and appearance (e.g. due to imaging modality, noise). FBA algorithms based on local invariant feature data are thus highly robust to nuisance variations in image data acquired from different sites, scanners and subjects. Furthermore, efficient feature indexing/matching algorithms (e.g. approximate nearest neighbor methods) serve as the basis for machine learning methods that scale to arbitrarily large image datasets, opening the door to “Big Data” style medical image analysis that improves as the number of training examples increases.