An Effective Method for Segmentation of MR Brain Images Using the Ant Colony Optimization Algorithm.
Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for class...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 6; pp. 1116 - 1124 |
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| Autores principales: | , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2013
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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=104153843&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104153843 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2013 vid: 26 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104153843 91842806 10.1007/s10278-013-9596-5 NLM23563793 104153843 ppf: 1116 ppct: 8 formats: fmt: @attributes: type: P tig: atl: An Effective Method for Segmentation of MR Brain Images Using the Ant Colony Optimization Algorithm. aug: au: Taherdangkoo, Mohammad Bagheri, Mohammad Yazdi, Mehran Andriole, Katherine affil: Taba Medical Imaging Center, 444 Felestin Street Shiraz Iran sug: subj: Magnetic Resonance Imaging Methods Brain Radiography Human Algorithms Methods Outcomes Research Evaluation ab: Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations; however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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