Automated perimetry allows examination of the visual field for diagnostic purposes. Location, shape and size of defects in the visual field detected during a perimetric examination are characteristic hints for the underlying disease of the visual system. Thus a reliable identification of defect types is essential for the proper treatment. We present a classifying system based on cluster analysis and Self-Organizing Maps for the automatic classification of visual field defects. The classifying system distinguishes between eight defect classes and was evaluated on over 8.800 perimetric examinations with a mean classification success of 78%. The classification algorithm is integrated into a software package that can be run on common computers using minor resources; its output can be considered as a suggestion for the physician. As the classification framework is decoupled from the perimetric hardware, it can also be used for the remote classification of perimetric examinations, e.g. in tele-medicine.
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