B D Madjarov1, J W Berger. 1. Computer Vision Laboratory, Scheie Eye Institute, University of Pennsylvania, Philadelphia 19104, USA.
Abstract
AIMS: Slit lamp fundus biomicroscopy allows for high magnification, stereoscopic diagnosis, and treatment of macular diseases. Variable contrast, narrow field of view, and specular reflections arising from the cornea, sclera, and examining lens reduce image quality; these images are of limited clinical utility for diagnosis, treatment planning, and photodocumentation when compared with fundus camera images. Algorithms are being developed to segment fundus imagery from slit lamp biomicroscopic video image sequences in order to improve clinical utility. METHODS: Video fundus image sequences of human volunteers were acquired with a video equipped, Nikon NS-1V slit lamp biomicroscope. Custom developed software identified specular reflections based on brightness and colour content, and extracted the illuminated fundus image based on colour image analysis and size constraints. RESULTS: In five subjects with variable image quality, the approach allowed for automatic, robust, accurate extraction of that portion of the video image corresponding to the illuminated portion of the fundus. Non-real time analysis allowed for fundus image segmentation for each frame of the image sequence. In real time, segmentation occurs at 2 Hz, and improvements are being implemented for video rate performance. CONCLUSIONS: Computer vision algorithms allow for real time extraction of fundus imagery from marginal quality, slit lamp fundus biomicroscope image sequences.
AIMS: Slit lamp fundus biomicroscopy allows for high magnification, stereoscopic diagnosis, and treatment of macular diseases. Variable contrast, narrow field of view, and specular reflections arising from the cornea, sclera, and examining lens reduce image quality; these images are of limited clinical utility for diagnosis, treatment planning, and photodocumentation when compared with fundus camera images. Algorithms are being developed to segment fundus imagery from slit lamp biomicroscopic video image sequences in order to improve clinical utility. METHODS: Video fundus image sequences of human volunteers were acquired with a video equipped, Nikon NS-1V slit lamp biomicroscope. Custom developed software identified specular reflections based on brightness and colour content, and extracted the illuminated fundus image based on colour image analysis and size constraints. RESULTS: In five subjects with variable image quality, the approach allowed for automatic, robust, accurate extraction of that portion of the video image corresponding to the illuminated portion of the fundus. Non-real time analysis allowed for fundus image segmentation for each frame of the image sequence. In real time, segmentation occurs at 2 Hz, and improvements are being implemented for video rate performance. CONCLUSIONS:Computer vision algorithms allow for real time extraction of fundus imagery from marginal quality, slit lamp fundus biomicroscope image sequences.