New model for medical image analysis based on computational intelligence

Medical image data contain several types of uncertain or vague information. Therefore, the knowledge about the data is also vague. Consequently, for the segmentation and classification of the image data the use of vague knowledge should be allowed. Finally, the optimization of such systems dealing with vague information should also be done automatically. Using fuzzy descriptions for the segmentation and analysis of medical image data has provided better results than the exclusive use of standard methods. More often, structures can be segmented and classified more adequately. Applications have been developed to segment brain structures and tumors in MRI- data. By applying neural networks, good results for the step of image improvement (pre-processing) and determination of regions of interest (ROI) in image data were obtained. Even more the classification of structures with neural networks has shown also good results. The optimization of the knowledge base was done with evolution strategies. Therefore, the optimization time was reduced to a fraction of the time needed for a manual optimization.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.