A Supervised Learning Approach for Crohn's Disease Detection Using Higher-Order Image Statistics and a Novel Shape Asymmetry Measure.
Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 5; pp. 920 - 932 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2013
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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=104229501&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104229501 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2013 vid: 26 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104229501 90397221 10.1007/s10278-013-9576-9 NLM23392736 PMC3782610 104229501 ppf: 920 ppct: 12 formats: fmt: @attributes: type: P tig: atl: A Supervised Learning Approach for Crohn's Disease Detection Using Higher-Order Image Statistics and a Novel Shape Asymmetry Measure. aug: au: Mahapatra, Dwarikanath Schueffler, Peter Tielbeek, Jeroen Buhmann, Joachim Vos, Franciscus affil: Department of Computer Science, ETH Zurich, Zurich Switzerland sug: subj: Crohn Disease Diagnosis Magnetic Resonance Imaging Image Interpretation, Computer Assisted Human Female Male Adult Middle Age Aged Data Analysis, Statistical ROC Curve T-Tests P-Value Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess rate of contrast enhancement and bowel wall thickness, and rely on extensive manual segmentation for accurate analysis. We propose a supervised learning method for the identification and localization of regions in abdominal magnetic resonance images that have been affected by CD. Low-level features like intensity and texture are used with shape asymmetry information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy-based shape asymmetry method and higher-order statistics like skewness and kurtosis. Multi-scale feature extraction renders the method robust. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods. 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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