Radiological Image and Information Processing Lab

The practice of Radiology is undergoing a radical transformation from one in which the primary result of an imaging examination is a written report addressing the reasons that the examination was ordered, to one in which the output is a (set of) quantitative measurement(s) with links to knowledge that could affect treatment.  For example, while a traditional report might have said “there is a mass in the right upper lobe of the lung,” the report of the future might say “The mass in the right upper lobe of the lung has grown by 25% since the last examination 3 months ago; it now measures 60 cc and has imaging features consistent with adenocarcinoma with an EGFR mutation that has has a favorable response to TK inhibitors. Click these links for similar cases and their clinical history. See references [1-4] for the latest articles of relevance.” Our lab, in collaboration with other IBIIS labs, radiologists, and other clinicians, and other collaborators from the School of Medicine, is involved in many aspects of creating that future, including advanced software for image visualization and quantitative analysis, image segmentation software that isolates regions within images for further analysis, software that extracts imaging features (e.g., shape, size, margin sharpness, pixel texture) within these regions, and algorithms for computing similarity between images and between patients as expressed by their images, demographic and clinical data.