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Integration of multiple knowledge sources in a system for brain CT-scan interpretation based on the blackboard model

机译:基于黑板模型的大脑CT扫描解释系统中的多种知识源集成

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Medical image interpretation is a complex task that requires the integration of knowledge acquired from different domains, such as medicine, computer vision and image processing. This paper describes a knowledge based brain CT scan interpretation system that uses the blackboard model to integrate various sources of knowledge. The frame-based representation technique is employed to represent the geometric model of the human brain. The knowledge on low level image processing algorithms and high level interpretation is partitioned into knowledge sources (KSs) that operate on and communicate through the domain blackboard. Several numeric image processing algorithms are coded into KSs that segment the images or extract features from the image primitives. For the mapping of image primitives to brain objects, there are two groups of mapping KSs, namely model-directed and data-directed. The system achieves the successful labeling and delineation of about 25 brain objects.
机译:医学图像解释是一项复杂的任务,需要整合从不同领域(例如医学,计算机视觉和图像处理)获得的知识。本文介绍了一种基于知识的脑CT扫描解释系统,该系统使用黑板模型来集成各种知识来源。基于帧的表示技术用于表示人脑的几何模型。有关低级图像处理算法和高级解释的知识分为知识源(KSs),这些知识源在域黑板上进行操作并通过域黑板进行通信。几种数字图像处理算法被编码到KS中,用于对图像进行分割或从图像基元中提取特征。为了将图像基元映射到大脑对象,有两组映射KS,即模型导向和数据导向。该系统成功完成了大约25个大脑对象的标记和描绘。

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