Automatic segmentation of carotid B-mode images using fuzzy classification.
This paper presents a new method for the automatic segmentation of the common carotid artery in B-mode images. This method uses the instantaneous coefficient of variation edge detector, fuzzy classification of edges and dynamic programming. Several discriminating features of the intima and adventiti...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 5; pp. 533 - 546 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
May2012
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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=104558675&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104558675 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2012 vid: 50 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104558675 NLM22415739 2011535467 10.1007/s11517-012-0883-y NLM22415739 104558675 ppf: 533 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Automatic segmentation of carotid B-mode images using fuzzy classification. aug: au: Rocha R Silva J Campilho A Rocha, Rui Silva, Jorge Campilho, Aurélio affil: INEB-Instituto de Engenharia Biomédica, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal sug: subj: Atherosclerosis Ultrasonography Carotid Artery Diseases Ultrasonography Carotid Arteries Ultrasonography Image Interpretation, Computer Assisted Methods Algorithms Diagnosis, Cardiovascular Logic Human ab: This paper presents a new method for the automatic segmentation of the common carotid artery in B-mode images. This method uses the instantaneous coefficient of variation edge detector, fuzzy classification of edges and dynamic programming. Several discriminating features of the intima and adventitia boundaries are considered, like the edge strength, the intensity gradient orientation, the valley shaped intensity profile and contextual information of the region delimited by those boundaries. The adopted fuzzy classification of edges helps avoiding low-pass filtering. The method is suited to real-time processing and user interaction is not required. Both the near and far wall boundaries can be detected in arteries with plaques of different types and sizes. Both expert manual and automatic tracings are significantly better for the far wall, due to the better visibility of the intima and adventitia boundaries. The automatic detection of the far wall shows an accuracy similar to the manual detections. For this wall, the error coefficient of variation for the mean intima-media thickness is in the range [5.6, 6.6 %] for automatic detections and in [6.7, 7.1 %] for manual ones. In the case of the near wall, the same coefficient of variation is in [11.2, 13.0 %] for automatic detections and in [5.9, 9.0 %] for manual detections. The mean intima-media thickness measurement errors observed for the far wall [Formula: see text] are among the best values reported for other fully automatic approaches. The application of this approach in clinical practice is encouraged by the results for the far wall and the short processing time (mean of 2.1 s per image). pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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