Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images.
An automatic method for cartilage segmentation using knee MRI images is described. Three binary classifiers with integral and partial pixel features are built using the Bayesian theorem to segment the femoral cartilage, tibial cartilage and patellar cartilage separately. First, an iterative procedur...
| Published in: | Journal of Digital Imaging Vol. 28; no. 6; pp. 695 - 704 |
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| Main Authors: | , , , , |
| Format: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110813326&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110813326 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2015 vid: 28 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 110813326 110813326 110813326 10.1007/s10278-015-9780-x NLM25700618 PMC4636712 110813326 ppf: 695 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images. aug: au: Pang, Jianfei Li, PengYue Qiu, Mingguo Chen, Wei Qiao, Liang affil: Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing China sug: subj: Cartilage, Articular Radiography Magnetic Resonance Imaging Knee Joint Radiography Radiographic Image Interpretation, Computer-Assisted Methods Cartilage, Articular Pathology Automation Comparative Studies Funding Source Prospective Studies Adult Human Adult: 19-44 years ab: An automatic method for cartilage segmentation using knee MRI images is described. Three binary classifiers with integral and partial pixel features are built using the Bayesian theorem to segment the femoral cartilage, tibial cartilage and patellar cartilage separately. First, an iterative procedure based on the feedback of the number of strong edges is designed to obtain an appropriate threshold for the Canny operator and to extract the bone-cartilage interface from MRI images. Second, the different edges are identified based on certain features, which allow for different cartilage to be distinguished synchronously. The cartilage is segmented preliminarily with minimum error Bayesian classifiers that have been previously trained. According to the cartilage edge and its anatomic location, the speed of segmentation is improved. Finally, morphological operations are used to improve the primary segmentation results. The cartilage edge is smooth in the automatic segmentation results and shows good consistency with manual segmentation results. The mean Dice similarity coefficient is 0.761. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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