| Paper: | ITT-P4.10 | ||
| Session: | Biomedical and biometric applications | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Industry Technology Track: Biomedical | ||
| Title: | A NEW KNOWLEDGE-BASED LUNG NODULE DETECTION SYSTEM | ||
| Authors: | Ravi Sankar; University of South Florida | ||
| Hongshun Su; University of South Florida | |||
| Wei Qian; University of South Florida | |||
| Xuejun Sun; University of South Florida | |||
| Abstract: | In this paper, we describe a knowledge-based system for segmenting and labeling lung nodule on CT images. The system was developed in a blackboard environment that incorporates lung knowledge model, image processing model and inference engine. Lung model, which contains anatomical knowledge about lung in the form of semantic networks, is used to guide the interpretation process. The system works in a hierarchical structure, from large structures to the final nodule candidates, by focusing on the interested region step by step. The symbolic variables introduced to accomplish high-level inference, are defined by fuzzy confidence functions in lung model. Composite fuzzy functions are used to map between image and lung model objects. Anatomical lung segments knowledge is embedded in the system to direct 3-D validation of suspicious objects. Structures are identified and abnormal objects are reported. Preliminary experiment results are included. | ||
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