A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors.
The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of pre...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 10; pp. 1 - 21 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925177&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925177 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2015 vid: 39 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925177 115925177 115925177 10.1007/s10916-015-0311-6 115925177 ppf: 1 ppct: 20 formats: fmt: @attributes: type: P tig: atl: A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors. aug: au: Zarinbal, M. Fazel Zarandi, M. Turksen, I. Izadi, M. affil: Department of Industrial Engineering, Amirkabir University of Technology, Tehran Iran sug: subj: Brain Neoplasms Diagnosis Brain Neoplasms Classification Glioma Diagnosis Glioma Classification Magnetic Resonance Imaging Knowledge Bases Algorithms Image Processing, Computer Assisted Human Cluster Analysis Automation Computer Memory Artificial Intelligence Poisson Distribution Repeated Measures Male Female Child Adolescence Adult Middle Age Aged Aged, 80 and Over Descriptive Statistics Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and 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. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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