DESIGN AND DEVELOPMENT OF A DESIGN TOOL FOR ENHANCED FLUORESCEIN ANGIOGRAPHY

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Intravenous Fluorescein Angiography (IVFA) is a well-established diagnostic tool which assists in planning laser treatments for ophthalmological disorders (e.g., neovascular age- related macular degeneration, diabetic retinopathy, and other disorders). The technology is a standard diagnostic tool required for all retina-related services offered by ophthalmology departments. IVFA produces a series of single frame, color filtered, monochrome views of the retinal circulation, where each individual frame is stored as a separate 35 mm slide or digital image. We present a novel computational framework with an interactive Graphical User Interface (GUI) that presents the series of processed, monochrome IVFA images as an animation. Due to cross-series inconsistencies caused by involuntary rapid eye movements, imaging from different angles, and lens distortion, the images should be registered in order to provide the best possible diagnosis of the IVFA procedure. The process involves four major computational step: a) image enhancement which identifies and replaces exudates and optic discs by average intensity pixel values in order to dismiss false alarms. Smoothening is then performed for noise removal, followed by contrast enhancement which aids in the sharpening of blood vessels for visual inspection; b) image registration is performed based on rigid body transforms. Compensation for rotation and translation is achieved with sub-pixel accuracy; c) the process is further reinforced by adding an informative false color to the registered image for discovery and interpretation of features of interest within retinal images. Labels are assigned to the connected objects in the image and then collected in the form of a matrix. The matrix is then converted into an RGB color image for ease of visualization; d) Graphical User Interface (GUI) will provide an autonomous view of the algorithmic results in an unified animated format. The developed computational framework provides easier viewing and inference of functional vascular defects by providing better models of retinal circulation and its physiology.