Computer aided diagnosis method for steatosis rating in ultrasound images using random forests.
Unlabelled: In this paper we discuss the problem of computer aided evaluation of the severity of steatosis disease using ultrasound images. The AIM of the study being to compare the automatic evaluation of liver steatosis using random forests (RF) and support vector machine (SVM) classifiers.Materia...
| Publicado en: | Medical Ultrasonography Vol. 15; no. 3; pp. 184 - 191 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Romanian Society of Ultrasonography in Medicine & Biology
Sep2013
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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=104090602&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104090602 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18444172 AX1S jtl: Medical Ultrasonography issn: 18444172 maglogo: N pubinfo: dt: Sep2013 vid: 15 iid: 3 pid: 57537 pub: Romanian Society of Ultrasonography in Medicine & Biology artinfo: ui: 104090602 NLM23979613 2012238523 10.11152/mu.2013.2066.153.dmm1vg2 NLM23979613 104090602 ppf: 184 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Computer aided diagnosis method for steatosis rating in ultrasound images using random forests. aug: au: Mihailescu, Dan Mihai Gui, Vasile Toma, Corneliu Ioan Popescu, Alina Sporea, Ioan Mihăilescu, Dan Mihai affil: Department of Telecommunications, Faculty of Electronics and Telecommunications, Politehnica University, Romania sug: subj: Data Analysis, Statistical Fatty Liver Epidemiology Fatty Liver Ultrasonography Image Interpretation, Computer Assisted Methods Algorithms Ultrasonography Statistics and Numerical Data Human Observer Bias Prevalence Reproducibility of Results Romania Sensitivity and Specificity ab: Unlabelled: In this paper we discuss the problem of computer aided evaluation of the severity of steatosis disease using ultrasound images. The AIM of the study being to compare the automatic evaluation of liver steatosis using random forests (RF) and support vector machine (SVM) classifiers.Material and Method: One hundred and twenty consecutive patients with steatosis or normal liver, assessed by ultrasound by the same expert, were enrolled. We graded steatosis in four stages and trained two classifiers to rate the severity of disease, based on a large set of labeled images and a large set of features, including several features obtained by robust estimation techniques. We compared RF and SVM classifiers. The classifiers were trained using cross-validation. There was 80% of data randomly selected for training and 20% for testing the classifier. This procedure was performed 20 times. The main measure of performance was the accuracy.Results: From all cases, 10 were rated as normal liver, 70 as having mild, 33 moderate, and 7 severe steatosis. Our best experts' ratings were used as ground truth data. RF outperformed the SVM classifier and confirmed the ability of this classifier to perform well without feature selection. In contrast, the performance of the SVM classifier was poor without feature selection and improved significantly after feature selection.Conclusion: The ability and accuracy of RF to classify well the steatosis severity, without feature selection, were superior as compared to SVM. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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