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In this paper we discuss a new approach to generate 3D models for a simulation system for training an angioplasty. The underlying data for these models are obtained from angiograms that are captured during routine interventions in cardiology. For the extraction of the arteries we use a non-linear classificatory with features based on vesselness information (using a scale-space approach), the gray value, and motion information of the arteries. As result we can correctly find 80 % of the arteries in the image and we have 4 % pixels incorrectly classified as arteries.
These models serve for a virtual catheter laboratory that is based on original instruments like catheters, wires, control instruments for the X-ray, syringes, and pressure pumps for the balloon catheter but instead of a patient an input instrument is used. This instrument sends positional and pressure data to a PC that simulates the patient. The cardiologist then obtains the visual and haptic feedback as if we operated a real patient.
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