Automatic Cardiac Segmentation Using Semantic Information from Random Forests.
We propose a fully automated method for segmenting the cardiac right ventricle (RV) from magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into superpixels and each superpixel is analyzed to detect the presence of RV regions using random forest (RF) classifiers. The su...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 6; pp. 794 - 805 |
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| Autor principal: | |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2014
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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=103912433&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103912433 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2014 vid: 27 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103912433 99255683 10.1007/s10278-014-9705-0 NLM24895064 103912433 ppf: 794 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Automatic Cardiac Segmentation Using Semantic Information from Random Forests. aug: au: Mahapatra, Dwarikanath affil: Department of Computer Science, Swiss Federal Institute of Technology, CAB E65.1, Universitatstrasse 6 Zurich 8092 Switzerland sug: subj: Radiographic Image Interpretation, Computer-Assisted Heart Ventricle, Right Radiography Magnetic Resonance Imaging Automation Algorithms Evaluation Research T-Tests P-Value Human ab: We propose a fully automated method for segmenting the cardiac right ventricle (RV) from magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into superpixels and each superpixel is analyzed to detect the presence of RV regions using random forest (RF) classifiers. The superpixels containing RV regions constitute the region of interest (ROI) which is used to segment the actual RV. Probability maps are generated for each ROI pixel using a second set of RF classifiers which give the probabilities of each pixel belonging to RV or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low-level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that compared to conventional method our algorithm achieves superior performance due to the inclusion of semantic knowledge and context information. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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