dc.contributor.author | Molinari, F. | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.contributor.author | Zeng, G. | en |
dc.contributor.author | Saba, L. | en |
dc.contributor.author | Acharya, U. R. | en |
dc.contributor.author | Sanfilippo, R. | en |
dc.contributor.author | Nicolaïdes, Andrew N. | en |
dc.contributor.author | Suri, J. S. | en |
dc.creator | Molinari, F. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.creator | Zeng, G. | en |
dc.creator | Saba, L. | en |
dc.creator | Acharya, U. R. | en |
dc.creator | Sanfilippo, R. | en |
dc.creator | Nicolaïdes, Andrew N. | en |
dc.creator | Suri, J. S. | en |
dc.date.accessioned | 2019-11-13T10:41:20Z | |
dc.date.available | 2019-11-13T10:41:20Z | |
dc.date.issued | 2012 | |
dc.identifier.issn | 1057-7149 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54556 | |
dc.description.abstract | The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management. © 2011 IEEE. | en |
dc.source | IEEE Transactions on Image Processing | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857322542&doi=10.1109%2fTIP.2011.2169270&partnerID=40&md5=6a375fefa2e4132326e2d1ab7843a66e | |
dc.subject | methodology | en |
dc.subject | article | en |
dc.subject | Algorithms | en |
dc.subject | human | en |
dc.subject | Humans | en |
dc.subject | algorithm | en |
dc.subject | Atherosclerosis | en |
dc.subject | echography | en |
dc.subject | Tunica Intima | en |
dc.subject | validation study | en |
dc.subject | Automation | en |
dc.subject | Low-complexity | en |
dc.subject | Diseases | en |
dc.subject | Ultrasonic imaging | en |
dc.subject | Cardio-vascular disease | en |
dc.subject | Carotid Arteries | en |
dc.subject | Data sets | en |
dc.subject | Database systems | en |
dc.subject | Databases, Factual | en |
dc.subject | Standard deviation | en |
dc.subject | Carotid artery | en |
dc.subject | First-order | en |
dc.subject | Image segmentation | en |
dc.subject | computer assisted diagnosis | en |
dc.subject | image enhancement | en |
dc.subject | Image Interpretation, Computer-Assisted | en |
dc.subject | Societies and institutions | en |
dc.subject | intima | en |
dc.subject | segmentation | en |
dc.subject | Intima-media thickness | en |
dc.subject | intima-media thickness (IMT) | en |
dc.subject | ultrasound imaging | en |
dc.subject | Manual segmentation | en |
dc.subject | tunica media | en |
dc.subject | Automated techniques | en |
dc.subject | Integrated approach | en |
dc.subject | Multi-resolutions | en |
dc.subject | Automated segmentation | en |
dc.subject | Clinical tools | en |
dc.subject | Disease management | en |
dc.subject | edge detection | en |
dc.subject | factual database | en |
dc.subject | Feature-based | en |
dc.subject | first-order absolute moment | en |
dc.subject | first-order Gaussian derivative | en |
dc.subject | Mean distances | en |
dc.subject | Measurement bias | en |
dc.subject | Scale-space | en |
dc.subject | Segmentation error | en |
dc.subject | Statistical classification | en |
dc.subject | Two stage | en |
dc.title | Completely automated multiresolution edge snapper-A new technique for an accurate carotid ultrasound IMT measurement: Clinical validation and benchmarking on a multi-institutional database | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1109/TIP.2011.2169270 | |
dc.description.volume | 21 | |
dc.description.issue | 3 | |
dc.description.startingpage | 1211 | |
dc.description.endingpage | 1222 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :37</p> | en |
dc.source.abbreviation | IEEE Trans.Image Process. | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |