Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7781
Title: Using Type-2 Fuzzy Function for Diagnosing Brain Tumors based on Image Processing Approach
Authors: Zarandi, Mohammad Hossein Fazel
Zarinbal, M.
Zarinbal, A.
Türkşen, İsmail Burhan
İzadi, M.
Keywords: Interval-Valued Type-2 Fuzzy Logic
Fuzzy Function
Image Processing
Brain Tumors Diagnosis
T-1-weighted MRI
Issue Date: 2010
Publisher: IEEE
Source: 2010 IEEE World Congress on Computational Intelligence -- JUL 18-23, 2010 -- Barcelona, SPAIN
Series/Report no.: IEEE International Conference on Fuzzy Systems
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 T-1-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.
URI: https://hdl.handle.net/20.500.11851/7781
ISBN: 978-1-4244-6920-8
ISSN: 1098-7584
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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