15 May 2003 Multi-agent IVUS image segmentation
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Abstract
A novel knowledge-based multi-agent image interpretation system has been developed which is markedly different from previous approaches in especially its elaborate integration of high-level knowledge-based control with low-level image segmentation algorithms. Each agent in this system is responsible for one type of object and cooperates with other agents to come to a consistent overall image interpretation. Cooperation involves communicating hypotheses and resolving conflicts between individual interpretations. Agents have full control over the underlying segmentation algorithms which they dynamically adapt to the image content given knowledge about global constraints, local information and personal beliefs. The system has been applied to IntraVascular Ultrasound (IVUS)images which are segmented by cooperative agents, specialized in lumen, vessel, calcified- plaque, shadow and sidebranch detection. IVUS image sequences from 7 patients were taken and vessel and lumen contours were detected fully automatically. These were compared with expert-corrected semi-automatic contours. Results show good correlation between agents and observer with r=0.84 for the lumen and r=0.92 for the vessel cross-sectional areas(n=1067). The paired difference between agents and observer was 0.13 ± 2.16 mm2 for vessel,and -0.14 ± 1.01mm2 for lumen cross-sectional areas. These results compare very well with inter-observer variability as reported in the literature.
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Ernst Bovenkamp, Jouke Dijkstra, Johan G. Bosch, Johan H. C. Reiber, "Multi-agent IVUS image segmentation", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480115; https://doi.org/10.1117/12.480115
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