A Novel Semiautomated Atherosclerotic Plaque Characterization Method Using Grayscale Intravascular Ultrasound Images: Comparison With Virtual Histology
| dc.contributor.author | Athanasiou, L. S. | en |
| dc.contributor.author | Karvelis, P. S. | en |
| dc.contributor.author | Tsakanikas, V. D. | en |
| dc.contributor.author | Naka, K. K. | en |
| dc.contributor.author | Michalis, L. K. | en |
| dc.contributor.author | Bourantas, C. V. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:34:53Z | |
| dc.date.available | 2015-11-24T17:34:53Z | |
| dc.identifier.issn | 1089-7771 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/14047 | |
| dc.rights | Default Licence | - |
| dc.subject | atherosclerotic plaque | en |
| dc.subject | plaque characterization | en |
| dc.subject | virtual histology | en |
| dc.subject | local binary patterns | en |
| dc.subject | coronary plaque | en |
| dc.subject | border detection | en |
| dc.subject | arterial-wall | en |
| dc.subject | classification | en |
| dc.subject | ivus | en |
| dc.subject | segmentation | en |
| dc.subject | networks | en |
| dc.subject | disease | en |
| dc.title | A Novel Semiautomated Atherosclerotic Plaque Characterization Method Using Grayscale Intravascular Ultrasound Images: Comparison With Virtual Histology | en |
| heal.abstract | Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with the VH software, cannot be characterized. To address these limitations, we have developed a plaque characterization technique that can be applied in grayscale IVUS images. Our semiautomated method is based on a three-step approach. In the first step, the plaque area [region of interest (ROI)] is detected semiautomatically. In the second step, a set of features is extracted for each pixel of the ROI and in the third step, a random forest classifier is used to classify these pixels into four classes: dense calcium, necrotic core, fibrotic tissue, and fibro-fatty tissue. In order to train and validate our method, we used 300 IVUS frames acquired from virtual histology examinations from ten patients. The overall accuracy of the proposed method was 85.65% suggesting that our approach is reliable and may be further investigated in the clinical and research arena. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | Doi 10.1109/Titb.2011.2181529 | - |
| heal.identifier.secondary | <Go to ISI>://000303997700012 | - |
| heal.journalName | Ieee Transactions on Information Technology in Biomedicine | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2012 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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