Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images.
In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 9; pp. 1563 - 1578 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2017
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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=124786154&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124786154 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2017 vid: 55 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124786154 124786154 144041063 NLM28160219 10.1007/s11517-017-1614-1 NLM28160219 124786154 ppf: 1563 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Automatic segmentation of left ventricle cavity from short-axis cardiac magnetic resonance images. aug: au: Yang, Xulei Song, Qing Su, Yi affil: Department of Computing Science, Institute of High Performance Computing , A*STAR , 1 Fusionopolis Way, Connexis Singapore 138632 Singapore sug: subj: Heart Ventricle Physiopathology Magnetic Resonance Imaging Methods Algorithms Information Science Image Interpretation, Computer Assisted Methods Clinical Assessment Tools Short Portable Mental Status Questionnaire ab: In this paper, a computational framework is proposed to perform a fully automatic segmentation of the left ventricle (LV) cavity from short-axis cardiac magnetic resonance (CMR) images. In the initial phase, the region of interest (ROI) is automatically identified on the first image frame of the CMR slices. This is done by partitioning the image into different regions using a standard fuzzy c-means (FCM) clustering algorithm where the LV region is identified according to its intensity, size and circularity in the image. Next, LV segmentation is performed within the identified ROI by using a novel clustering method that utilizes an objective functional with a dissimilarity measure that incorporates a circular shape function. This circular shape-constrained FCM algorithm is able to differentiate pixels with similar intensity but are located in different regions (e.g. LV cavity and non-LV cavity), thus improving the accuracy of the segmentation even in the presence of papillary muscles. In the final step, the segmented LV cavity is propagated to the adjacent image frame to act as the ROI. The segmentation and ROI propagation are then iteratively executed until the segmentation has been performed for the whole cardiac sequence. Experiment results using the LV Segmentation Challenge validation datasets show that our proposed framework can achieve an average perpendicular distance (APD) shift of 2.23 ± 0.50 mm and the Dice metric (DM) index of 0.89 ± 0.03, which is comparable to the existing cutting edge methods. The added advantage over state of the art is that our approach is fully automatic, does not need manual initialization and does not require a prior trained model. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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