Image-based clustering and connected component labeling for rapid automated left and right ventricular endocardial volume extraction and segmentation in full cardiac cycle multi-frame MRI images of cardiac patients.
A rapid method for left and right ventricular endocardial volume segmentation and clinical cardiac parameter calculation from MRI images of cardiac patients is presented. The clinical motivation is providing cardiologists a tool for assessing the cardiac function in a patient through the left ventri...
| Published in: | Medical & Biological Engineering & Computing Vol. 57; no. 6; pp. 1213 - 1229 |
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| Format: | Journal Article |
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Springer Nature
Jun2019
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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=136505524&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136505524 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2019 vid: 57 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136505524 136505524 NLM30690663 10.1007/s11517-019-01952-9 NLM30690663 136505524 ppf: 1213 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Image-based clustering and connected component labeling for rapid automated left and right ventricular endocardial volume extraction and segmentation in full cardiac cycle multi-frame MRI images of cardiac patients. aug: au: Goyal, Ayush affil: Department of Electrical Engineering and Computer Science, Frank H. Dotterweich College of Engineering, Texas A&M University – Kingsville, MSC 192, 700 University Blvd., 78363-8202, Kingsville, TX, USA sug: subj: Image Processing, Computer Assisted Algorithms Magnetic Resonance Imaging Heart Ventricle Endocardium Staining and Labeling Automation Scales ab: A rapid method for left and right ventricular endocardial volume segmentation and clinical cardiac parameter calculation from MRI images of cardiac patients is presented. The clinical motivation is providing cardiologists a tool for assessing the cardiac function in a patient through the left ventricular endocardial volume's ejection fraction. A new method combining adapted fuzzy membership-based c-means pixel clustering and connected regions component labeling is used for automatic segmentation of the left and right ventricular endocardial volumes. This proposed pixel clustering with labeling approach avoids manual initialization or user intervention and does not require specifying the region of interest. This method fully automatically extracts the left and right ventricular endocardial volumes and avoids manual tracing on all MRI image frames in the complete cardiac cycle from systole to diastole. The average computational processing time per frame is 0.6 s, making it much more efficient than deformable methods, which need several iterations for the evolution of the snake or contour. Accuracy of the automated method presented herein was validated against manual tracing-based extraction, performed with the guidance of cardiac experts, on several MRI frames. Dice coefficients between the proposed automatic versus manual traced ventricular endocardial volume segmentations were observed to be 0.9781 ± 0.0070 (for left ventricular endocardial volume) and 0.9819 ± 0.0058 (for right ventricular endocardial volume), and the Pearson correlation coefficients were observed to be 0.9655 ± 0.0206 (for left ventricular endocardial volume) and 0.9870 ± 0.0131 (for right ventricular endocardial volume). Graphical abstract The left ventricular endocardial volume segmentation methodology illustrated as a series of algorithms. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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