摘要
目的提高图像分割技术的自动化程度和可靠性。方法提出了一个基于知识模型的医学CT图的分割方法。系统由解剖知识模型、图像处理程序和推理机组成。模块之间的通讯由黑板控制。结通过在胸部CT图像处理中的应用,该方法减少了人工干预,得到较满意的分割结果。结论该方法提了医学图像分割的自动化程度和可靠性。由于具有扩展性,该方法为基于知识医学图像的处理提供个通用的模式。
Objective To improve the automatization and reliability of medical image segmentation. Method An anatomical model was built and used to guide the low-level segmentation process. The system architecture was made up of an anatomical model,image processing routines and an inference engine,the interaction of which are governed by a blackboard.Result The result of application of the segmentation for chest CT image was satisfactory and needs less operator intensive.Conclusion This method improves automatization and reliability of the medical image segmentation. Because of the good expansibility ,it may serve as a template for knowledge-based processing of medical image.
出处
《航天医学与医学工程》
CAS
CSCD
北大核心
2005年第1期62-65,共4页
Space Medicine & Medical Engineering
基金
国家863高技术研究发展计划(2002AA413710)
关键词
知识模型
医学图像
图像分割
推理机
kowledge models, medical image
image segmentation
inference engine
