Abstract
Fluorescein video angiographies (FVAs) are a diagnostic tool for eye diseases, such as diabetic retinopathy (DR). Currently, kinetic tracer model methods based on indicator-dilutions theory use FVAs to extract biomarkers (e.g., volumetric blood flow and retinal vascular permeability) via pixel mapping using two-step non-linear least square fitting. Prior to biomarker extraction, the FVAs must attain optimal quality. The objective of this research is to create a program to remove all frames experiencing signal drops (causes include blinking, squinting, and head movement). 15 FVAs (6 healthy control subjects, 6 diabetes mellitus no DR (DMnoDR) subjects, and 3 mild non-proliferative DR (NPDR) subjects) were analyzed for low quality frames. The average signal of each frame was analyzed as top, middle, and bottom thirds. The frame with maximum average signal up to the final frame of a created "Gold Standard" was compared with the raw AVI's frame with maximum average signal and subsequent frames. All frames before maximum average signal and any remaining frames were compared with the previous good-quality raw frame to determine if the frame of interest was of good quality. All remaining frames were subsequently re-evaluated and flagged if they had a local minimum prominence of 10% of the maximum average signal. The flagged frames', as well as former and subsequent frames', quality were subjectively determined. The AVI quality was subsequently tested via pre-DTKM processing and biomarker extraction via DTKM methods. Results displayed that the semi-automated frame removal process provides sufficient quality AVIs.