• 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