DocumentCode
230118
Title
Interval type-2 fuzzy image processing expert system for diagnosing brain tumors
Author
Zarinbal, M. ; Fazel Zarandi, M.H. ; Turksen, I.B. ; Izadid, M.
Author_Institution
Dept. of Ind. Eng., Amirkabir Univ. of Technol., Tehran, Iran
fYear
2014
fDate
24-26 June 2014
Firstpage
1
Lastpage
8
Abstract
The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an Interval Type-2 fuzzy image processing expert system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with four steps of preprocessing, segmentation, feature extraction and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. The performance of this system is evaluated using 100 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.
Keywords
biomedical MRI; brain; feature extraction; fuzzy set theory; image segmentation; inference mechanisms; medical expert systems; medical image processing; tumours; MRI scans; approximate reasoning; astrocytomas diagnosis; astrocytomas differentiation; brain tumor diagnosis; feature extraction; inference engine; interval type-2 fuzzy image processing expert system; knowledge base; preprocessing; segmentation; working memory; Engines; Expert systems; Feature extraction; Magnetic resonance imaging; Tumors; Astrocytomas; Collaborative Fuzzy Clustering; Image Processing; Interval Type-2 Fuzzy Logic; Medical Expert System;
fLanguage
English
Publisher
ieee
Conference_Titel
Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on
Conference_Location
Boston, MA
Type
conf
DOI
10.1109/NORBERT.2014.6893890
Filename
6893890
Link To Document