Medical image analysis and representation using a fuzzy and rule-based hybrid approach
When missing, ambiguous or distorted data is available in digital image processing, soft computing (e.g. fuzzy logic, neural networks, evolutionary computation) has proved to yield promising results. In the field of biomedical image analysis, when information has a strong structural character, many methods of artificial intelligence are well suited for knowledge discovery, representation and processing. Fuzzy logic acts as an unified framework for representing and processing both numerical and symbolic information, as well as structural information constituted mainly by spatial relationships in biomedical imaging. This paper describes the use of fuzzy logic at low level to a higher level (e.g. model based structural pattern recognition and scene understanding). Applications are for segmentation of brain structures in magnetic resonance (MR) and CT (computer tomography) images, based both on atlas and real data. Promising results show the superiority of this knowledge-based approach over best traditional techniques in terms of segmentation errors.
medical imaging fuzzy logic image representation and segmentation knowledge-based systems