An automatic multi-class coronary atherosclerosis plaque detection and classification framework.
Detection of different classes of atherosclerotic plaques is important for early intervention of coronary artery diseases. However, previous methods focused either on the detection of a specific class of coronary plaques or on the distinction between plaques and normal arteries, neglecting the class...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 1; pp. 245 - 258 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2019
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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=133800694&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133800694 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2019 vid: 57 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133800694 133800694 NLM30088125 10.1007/s11517-018-1880-6 NLM30088125 133800694 ppf: 245 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An automatic multi-class coronary atherosclerosis plaque detection and classification framework. aug: au: Zhao, Fengjun Wu, Bin Chen, Fei Cao, Xin Yi, Huangjian Hou, Yuqing He, Xiaowei Liang, Jimin affil: School of Information Sciences and Technology, Northwest University, 710069, Xi'an, Shaanxi, China sug: subj: Coronary Arteriosclerosis Diagnosis Atherosclerosis Diagnosis Atherosclerosis Classification Coronary Arteriosclerosis Atherosclerosis Image Processing, Computer Assisted Reproducibility of Results Databases Automation Algorithms ab: Detection of different classes of atherosclerotic plaques is important for early intervention of coronary artery diseases. However, previous methods focused either on the detection of a specific class of coronary plaques or on the distinction between plaques and normal arteries, neglecting the classification of different classes of plaques. Therefore, we proposed an automatic multi-class coronary atherosclerosis plaque detection and classification framework. Firstly, we retrieved the transverse cross sections along centerlines from the computed tomography angiography. Secondly, we extracted the region of interests based on coarse segmentation. Thirdly, we extracted a random radius symmetry (RRS) feature vector, which incorporates multiple descriptions into a random strategy and greatly augments the training data. Finally, we fed the RRS feature vector into the multi-class coronary plaque classifier. In experiments, we compared our proposed framework with other methods on the cross sections of Rotterdam Coronary Datasets, including 729 non-calcified plaques, 511 calcified plaques, and 546 mixed plaques. Our RRS with support vector machine outperforms the intensity feature vector and the random forest classifier, with the average precision of 92.6 ± 1.9% and average recall of 94.3 ± 2.1%. The proposed framework provides a computer-aided diagnostic method for multi-class plaque detection and classification. Graphical abstract Diagram of the proposed automatic multi-class coronary atherosclerosis plaque detection and classification framework. ᅟ. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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