Computer-Aided Diagnosis for Phase-Contrast X-ray Computed Tomography: Quantitative Characterization of Human Patellar Cartilage with High-Dimensional Geometric Features.
Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2)...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 1; pp. 98 - 108 |
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| Autores principales: | , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2014
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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=104013604&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104013604 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2014 vid: 27 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104013604 94061943 10.1007/s10278-013-9634-3 NLM24043594 PMC3903967 104013604 ppf: 98 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Computer-Aided Diagnosis for Phase-Contrast X-ray Computed Tomography: Quantitative Characterization of Human Patellar Cartilage with High-Dimensional Geometric Features. aug: au: Nagarajan, Mahesh Coan, Paola Huber, Markus Diemoz, Paul Glaser, Christian Wismüller, Axel affil: Department of Biomedical Engineering, University of Rochester, 430 Elmwood Ave Rochester 14627 USA sug: subj: Diagnosis, Computer Assisted Tomography, X-Ray Computed Methods Cartilage, Articular Radiography Osteoarthritis, Knee Radiography Chondrocytes Radiography Osteoarthritis, Knee Diagnosis Patella Cadaver Cartilage, Articular Pathology Evaluation Research ROC Curve Wilcoxon Signed Rank Test Data Analysis Software P-Value Human Funding Source ab: Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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