MRI Brain Abnormality Detection Using Fuzzy Neural Networks
Paper in proceeding, 1996

In this paper an expert system for detection of brain abnormalities is proposed. First preceding methods for segmentation of MR images are reviewed and their limitations are discussed. In the proposed method, MR images (three images from one slice: T1, T2 and Proton Density) are acquired from a scanner or directly from MRI system. For noise deletation two filters (median and bandreject lowpass) are used (This stage is optional). They make a clean view of MR images. It is necessary to have precise detections. So by implementing a gray-scale to color transformation algorithm (it is a radially symmetric butterworth band-reject filter), system can recognize the differences between tissues accurately. Now we have three colored images (T1, T2 and Proton Density) from the last section that better represent tissues and it is possible to say that those tissues with the same color in each of these three images may be same tissues. The combination of fuzzy systems and neural Networks make a powerful tool for pattern recognition problems. So a fuzzified neural network with outputs to a back-propagation network for tissues recognition must be used. Therefore a fuzzy neuron and a fuzzified network are introduced. The output of the back-propagation network is the type of tissue under process. The results of the last section are fed into another network that uses a knowledge-base to make a suggestion for treatment (This level is also optional). Because of the time limitation MATLAB 4.0 for Windows is used for expert system simulation. It has several abilities for matrix calculations and graphing that make the work easier (some of the base modules are written in C++).

pattern recognition

Fuzzy neural networks



Kasra Haghighi

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

11th Intl. Conf. of Applications of Artificial Intelligence in Engineering

1853124109 (ISBN)

Subject Categories

Human Computer Interaction

Computer Vision and Robotics (Autonomous Systems)



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