Sensor integration for tomographic image segmentation

Abstract

An expert vision system is proposed which integrates knowledge from diverse sources for tomographic image segmentation. The system mimicks 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 computed tomography, magnetic resonance imaging, and 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.<>