A Metaheuristically Tuned Interval Type 2 Fuzzy System to Reduce Segmentation Uncertainty in Brain MRI Images.
Precise segmentation of magnetic resonance image (MRI) seems challenging because of the complex structure of the brain, non-uniform field in images, and noise. As a result, decision-making is associated with uncertainty. Fuzzy based approaches have been developed to overcome this problem, though mos...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 11; pp. 1 - 15 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2017
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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=125950747&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125950747 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2017 vid: 41 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125950747 125950747 125950747 10.1007/s10916-017-0821-5 125950747 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: A Metaheuristically Tuned Interval Type 2 Fuzzy System to Reduce Segmentation Uncertainty in Brain MRI Images. aug: au: Taghribi, Abolfazl Sharifian, Saeed affil: Department of Electrical Engineering , Amirkabir University of Technology , Tehran 15914 Iran sug: subj: Magnetic Resonance Imaging Brain Algorithms Human Simulations ab: Precise segmentation of magnetic resonance image (MRI) seems challenging because of the complex structure of the brain, non-uniform field in images, and noise. As a result, decision-making is associated with uncertainty. Fuzzy based approaches have been developed to overcome this problem, though most of them use fuzzy type 1 method, and sometimes contain a pre-processing step. This paper 'modified type 2 fuzzy system' (MT2FS) declares a state-of-the-art method to segment MRI images using interval fuzzy type-2. Furthermore, Genetic algorithm has been employed to specify the best values for mean and variance of upper and lower membership functions. This strategy will determine discrimination boundaries for different brain tissues to be less independent from the training set. Finally, the result of fuzzy rules is extracted by using Dempster-Shafer rule combination method. Simulation results demonstrate a satisfactory output on both simulated and real MRI images in comparison with previously conducted research works without the need for a pre-processing stage. 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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