In this paper, an expert vision system is proposed which integrates knowledge from diverse sources for tomographic image segmentation. The system miinicks the reasoning process of an expert to divide a tomographic brain image into semantically meaningful entities. These entities can then be related to the fundamental biomedical processes, both in health and in disease, that are of interest or of importance to health care research. The images under study include those acquired from x-ray CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and PET (Positron Emission Tomography). Given a set of three (correlated) images acquired from these three different modalities at the same slicing level and angle of a human brain, the proposed system performs image segmentation based on (1) knowledge about the characteristics of the three different sensors, (2) knowledge about the anatomic structures of human brains, (3) knowledge about brain diseases, and (4) knowledge about image processing and analysis tools. Since the problem domain is characterized by incomplete and uncertain information, the blackboard architecture which is an opportunistic reasoning model is adopted as the framework of the proposed system.