Learn More
OBJECTIVE To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. METHODS Gaze data and diagnostic decisions were collected from three breast imaging radiologists and three radiology residents who reviewed 20 screening mammograms while wearing a head-mounted(More)
In this study, we aim to determine if iris recognition accuracy might be improved by correcting for the refrac-tive effects of the human eye when the optical axes of the eye and camera are misaligned. We undertake this investigation using an anatomically-approximated, three-dimensional model of the human eye and ray-tracing. We generate synthetic iris(More)
The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV) catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms, however, this process is to be replaced by an automated system. Experiments to determine the best NIR wavelengths to optimize vein contrast(More)
In a telemedicine environment for retinopathy screening, a quality check is needed on initial input images to ensure sufficient clarity for proper diagnosis. This is true whether the system uses human screeners or automated software for diagnosis. We present a method for the detection of flash artifacts found in retina images. We have collected a set of(More)
In this work we propose a method to simulate the expected, i.e. seen by a camera, multispectral reflectance images of a large skin surface area by combining Monte Carlo light propagation model and realistic tissue modeling based on three dimensional data acquisition of human body areas. In particular, we aim to simulate more accurately light transport in(More)
  • 1