Bayesian belief networks for medical image recognition
Chien-Shung Hwang, Wei‐Chung Lin, Chin-Tu Chen, Shiuh-Yung J. Chen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
In this paper, we propose the interval-based Bayesian belief networks and then use them as the inference scheme in a medical image recognition system. To integrate knowledges from various sources, the blackboard architecture is used as the framework. The proposed system consists of three phases. In phase one, three correlated images acquired from x-ray CT, proton density and T2-weighted MRI of a human brain are presented to the system. A signal-based segmentation algorithm is then employed to divide each image into regions of homogeneous attributes. In phase two, the system tries to identify the major anatomical structures and locate the slice in the model that is most similar to the image set under study. To accomplish this work, one Bayesian belief network is constructed to integrate evidence from various sensor slices and the feature spaces for each anatomy and the other belief network is designed for opportunistic control in the blackboard system. In phase three, the selected model slice is used to guide the process of refining the recognized anatomies.