The application of fuzzy-based methods to central nerve fiber imaging

Abstract

This paper discusses the potential of fuzzy logic methods within medical imaging. Technical advances have produced imaging techniques that can visualize structures and their functions in the living human body. The interpretation of these images plays a prominent role in diagnostic and therapeutic decisions, so physicians must deal with a variety of image processing methods and their applications. This paper describes three different sources of medical imagery that allow the visualization of nerve fibers in the human brain: (1) an algorithm for automatic segmentation of some parts of the thalamus in magnetic resonance images based on the differences in myelin content in various thalamic subnuclei; (2) polarized light for classifying the 3D orientation of the nerve fibers at each point; and (3) confocal laser scanning microscopy (CLSM) for calculating semiquantitative variables for myelin content. Fuzzy logic methods were applied to analyze these pictures from low- to high-level image processing. The solutions presented here are motivated by problems of routine neuroanatomic research demonstrating fuzzy-based methods to be valuable tools in medical image processing.