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Using type-2 fuzzy function for diagnosing brain tumors based on image processing approach
Fazel Zarandi, M. H ; Sharif University of Technology | 2010
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- Type of Document: Article
- DOI: 10.1109/FUZZY.2010.5584469
- Publisher: 2010
- Abstract:
- 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
- Keywords:
- Interval-valued type-2 fuzzy logic ; Approximate reasoning ; Astrocytoma ; Brain tumors ; Brain tumors diagnosis ; Computation steps ; Defuzzification method ; Function identification ; Fuzzifications ; Fuzzy function ; Fuzzy image processing ; Fuzzy rule-base ; Implication operators ; In-depth understanding ; Interval-valued ; Least square estimates ; MR images ; Pre-processing ; Processing approach ; System models ; System-based ; T - Norm ; T1-weighted MRI ; Type-2 fuzzy functions ; Artificial intelligence ; Computational complexity ; Diagnosis ; Expert systems ; Feature extraction ; Fuzzy logic ; Fuzzy set theory ; Fuzzy systems ; Image processing ; Imaging systems ; Maximum likelihood estimation ; Tumors ; Membership functions
- Source: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, 18 2010 through 23 July 2010 ; July , 2010 ; 9781424469208 (ISBN)
- URL: http://ieeexplore.ieee.org/document/5584469/?reload=true