Lehrstuhl für Informatik 5 (Mustererkennung), University Erlangen-Nürnberg, West Germany.
IEEE Trans Pattern Anal Mach Intell. 1985 Mar;7(3):246-59. doi: 10.1109/tpami.1985.4767655.
A system for obtaining a complete diagnostic description of an image sequence taken in nuclear medicine from the human heart has been developed, implemented, and tested. The knowledge about these images is represented in a semantic net, conclusions are drawn by a production rule approach, and scoring of alternative diagnoses is based on fuzzy membership functions. On the low level, image pixels are smoothed and organ contours are extracted; these are the input for the high level processing. Tests with several image sequences gave correct descriptions as compared to the diagnosis of a physician.
已经开发、实现和测试了一种用于从人体心脏的核医学图像序列中获取完整诊断描述的系统。这些图像的知识表示在语义网络中，结论通过产生式规则方法得出，替代诊断的评分基于模糊隶属函数。在低水平，图像像素被平滑化并且器官轮廓被提取；这些是高水平处理的输入。与医生的诊断相比，对几个图像序列的测试给出了正确的描述。
