Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model.
The study introduces a novel method for automatic segmentation of vertebral column tissue from MRI images. The paper describes a method that combines multiple stages of Machine Learning techniques to recognize and separate different tissues of the human spine. For the needs of this paper, 50 MRI exa...
| Publicado en: | BioMed Research International Vol. 2018; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
Wiley-Blackwell
4/29/2018
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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=129350967&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129350967 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/29/2018 vid: 2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 129350967 129350967 129350967 10.1155/2018/7952946 129350967 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Spine Tissue Segmentation from MRI Data Based on Cascade of Boosted Classifiers and Active Appearance Model. aug: au: Gaweł, Dominik Główka, Paweł Kotwicki, Tomasz Nowak, Michał affil: Chair of Virtual Engineering, Poznań University of Technology, 60-965 Poznań, Poland sug: subj: Lumbar Vertebrae Ultrasonography Magnetic Resonance Imaging Methods Machine Learning Models, Structural Validation Studies Record Review Human Low Back Pain Algorithms Descriptive Statistics Data Analysis Software Intraclass Correlation Coefficient P-Value ab: The study introduces a novel method for automatic segmentation of vertebral column tissue from MRI images. The paper describes a method that combines multiple stages of Machine Learning techniques to recognize and separate different tissues of the human spine. For the needs of this paper, 50 MRI examinations presenting lumbosacral spine of patients with low back pain were selected. After the initial filtration, automatic vertebrae recognition using Cascade Classifier takes place. Afterwards the main segmentation process using the patch based Active Appearance Model is performed. Obtained results are interpolated using centripetal Catmull–Rom splines. The method was tested on previously unseen vertebrae images segmented manually by 5 physicians. A test validating algorithm convergence per iteration was performed and the Intraclass Correlation Coefficient was calculated. Additionally, the 10-fold cross-validation analysis has been done. Presented method proved to be comparable to the physicians (FF=90.19±1.01%). Moreover results confirmed a proper algorithm convergence. Automatically segmented area correlated well with manual segmentation for single measurements (r¯=0.8336) and for average measurements (r¯=0.9068) with p=0.05. The 10-fold cross-validation analysis (FF=91.37±1.13%) confirmed a good model generalization resulting in practical performance. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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