Using Type-2 fuzzy function for diagnosing brain tumors based on image processing approach

Fuzzy functions are used to identify the structure of system models and reasoning with them. Fuzzy functions can be determined by any function identification method such as Least Square Estimates (LSE), Maximum Likelihood Estimates (MLE) or Support Vector Machine Estimates (SVM). However, estimating fuzzy functions using LSE method is structurally a new and unique approach for determining fuzzy functions. By using this approach, there is no need to know or to develop an in-depth understanding of essential concepts for developing and using the membership functions and selecting the t-norms, co-norms and implication operators. Furthermore, there is no need to apply fuzzification and defuzzification methods. The goal of this paper is to improve the Type-2 fuzzy image processing expert system based on Type-2 fuzzy function to diagnose the Astrocytoma tumors (most important category of brain tumors) in T₁-weighted MR Images with contrast. This expert system has four steps, Pre-processing, Segmentation, Feature extraction and Approximate reasoning. The focus of this paper is to improve the last step, Approximate reasoning step, by using fuzzy function strategy instead of fuzzy rule-base approach. The results show that Type-2 fuzzy function approach requires less computation steps with less computational complexity and could provide better results.