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Modern imaging systems generate enormous volumes of data, far exceeding the human capacity for interpretation—a manifestation of big data in biomedical imaging. However, it is not the images themselves, but rather the clinically relevant information contained within them, that is paramount. Clearly, in response to this big data challenge, it is critical to develop novel computational approaches for gleaning such information from various biomedical images.
Our research lies at the intersection of computer science, radiology, cardiology, biology, pathology, etc. Drawing upon computer vision, computer graphics, machine learning, and mathematics, our research focus is on the development and validation of novel computational methodologies for the extraction, analysis and quantification of structural, functional, cellular, and molecular information from images acquired through various imaging modalities, and for the integration of image-based information with non-image-based information with the objective of improving the efficiency and accuracy of image interpretation for diagnosis, surgery, therapy and drug discovery.
Imaging informatics touches every aspect of the imaging chain from image creation and acquisition; to image distribution and management; to image storage and retrieval; to image processing, analysis and understanding; to image visualization and data navigation; to image interpretation, reporting, and communications.