基于角点检测的可降解支架轮廓分割算法
Corner Detection-Based Segmentation Algorithm of Bioresorbable Vascular Scaffold Strut Contours
查看参考文献23篇
文摘
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针对血管内光学相干断层扫描(IVOCT)影像中,使用动态规划(DP)算法进行可降解支架轮廓分割时,分割结果容易受到血液伪影和支架内部断裂的影响,而导致支架轮廓分割准确度不高的问题,利用IVOCT影像中可降解支架具有四边形外观的先验信息,提出一种使用支架的4个角点得到支架轮廓的算法。实验结果显示:所提出的支架轮廓分割算法的平均Dice系数可达到0.88,相较于DP算法提高了0.08;所提出的支架自动分割算法能够实现IVOCT影像中可降解支架的准确分割,且具有较好的稳健性,能更好地在临床应用中辅助医生进行支架贴壁情况分析。 |
其他语种文摘
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According to the prior knowledge about obvious quadrilateral feature of bioresorbable vascular scaffold (BVS) struts in an intravascular optical coherence tomography (IVOCT) image, this study proposes a novel algorithm based on four corners of BVS struts to automatically obtain their contours in the IVOCT imaging system. It solves the problem that dynamic programming (DP) algorithm, which is a contour-based algorithm, is not sufficiently accurate because of the influence of the fractures inside the struts and blood artifacts around the struts. Experimental results show that the proposed algorithm achieves an average Dice's coefficient of 0.88 for the strut segmentation areas, which is increased by approximately 0.08 compared to the result obtained by the DP algorithm. This algorithm can accurately and robustly segment BVS struts in the IVOCT image, and thus it can better assist doctors in the automatic strut malapposition analysis in clinical applications. |
来源
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光学学报
,2019,39(7):0715001 【核心库】
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DOI
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10.3788/AOS201939.0715001
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关键词
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机器视觉
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角点检测
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轮廓自动分割
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贴壁情况分析
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可降解支架
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血管内光学相干断层扫描图像
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地址
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1.
中国科学院西安光学精密机械研究所, 瞬态光学与光子技术国家重点实验室, 陕西, 西安, 710119
2.
中国科学院大学, 北京, 100049
3.
中国人民解放军总医院心血管内科, 北京, 100853
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语种
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中文 |
文献类型
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研究性论文 |
ISSN
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0253-2239 |
学科
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自动化技术、计算机技术 |
基金
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国家科技支撑计划项目
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文献收藏号
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CSCD:6548044
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参考文献 共
23
共2页
|
1.
Forouzanfar M H. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015.
The Lancet,2016,388(10053):1659-1724
|
CSCD被引
31
次
|
|
|
|
2.
Gurajala I. Perioperative management of patient with intracoronary stent presenting for noncardiac surgery.
Annals of Cardiac Anaesthesia,2016,19(1):122-131
|
CSCD被引
2
次
|
|
|
|
3.
Kereiakes D J. Bioresorbable vascular scaffolds for coronary revascularization.
Circulation,2016,134(2):168-182
|
CSCD被引
6
次
|
|
|
|
4.
Kotani J I. Incomplete neointimal coverage of sirolimus-eluting stents.
Journal of the American College of Cardiology,2006,47(10):2108-2111
|
CSCD被引
19
次
|
|
|
|
5.
Gogas B D. The ABSORB bioresorbable vascular scaffold: an evolution or revolution in interventional cardiology?.
Hellenike Kardiologike Epitheorese,2012,53(4):301-309
|
CSCD被引
1
次
|
|
|
|
6.
Serruys P W. A bioabsorbable everolimus-eluting coronary stent system (ABSORB): 2-year outcomes and results from multiple imaging methods.
The Lancet,2009,373(9667):897-910
|
CSCD被引
24
次
|
|
|
|
7.
Gonzalo N. Optical coherence tomography (OCT) in secondary revascularisation: stent and graft assessment.
EuroIntervention,2009,5(Suppl D):D93-D100
|
CSCD被引
1
次
|
|
|
|
8.
Tearney G J. Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the international working group for intravascular optical coherence tomography standardization and validation.
Journal of the American College of Cardiology,2012,59(12):1058-1072
|
CSCD被引
46
次
|
|
|
|
9.
Wang A C. Automatic detection of bioresorbable vascular scaffold struts in intravascular optical coherence tomography pullback runs.
Biomedical Optics Express,2014,5(10):3589-3602
|
CSCD被引
3
次
|
|
|
|
10.
鲁逸峰. 基于机器学习的可降解支架检测与分割算法.
光学学报,2018,38(2):0215005
|
CSCD被引
5
次
|
|
|
|
11.
Lu Y F. Adaboost-based detection and segmentation of bioresorbable vascular scaffolds struts in IVOCT images.
2017 IEEE International Conference on Image Processing (ICIP),September 17-20, 2017, Beijing, China,2017:4432-4436
|
CSCD被引
1
次
|
|
|
|
12.
Moravec H P.
Obstacle avoidance and navigation in the real world by a seeing robot rover,1980
|
CSCD被引
17
次
|
|
|
|
13.
Harris C G. A combined corner and edge detector.
Proceedings of 4th Alvey Vision Conference. 15(50),1988:10-5244
|
CSCD被引
1
次
|
|
|
|
14.
Smith S M. SUSAN—a new approach to low level image processing.
International Journal of Computer Vision,1997,23(1):45-78
|
CSCD被引
436
次
|
|
|
|
15.
Ojala T. A comparative study of texture measures with classification based on featured distributions.
Pattern Recognition,1996,29(1):51-59
|
CSCD被引
419
次
|
|
|
|
16.
Lienhart R. An extended set of Haar-like features for rapid object detection.
Proceedings of International Conference on Image Processing, September 22-25, 2002,Rochester, NY, USA. I,2002:900-903
|
CSCD被引
1
次
|
|
|
|
17.
Dalal N. Histograms of oriented gradients for human detection.
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), June 20-25,2005, San Diego, CA, USA,2005:886-893
|
CSCD被引
1
次
|
|
|
|
18.
Bay H. Speeded-up robust features (SURF).
Computer Vision and Image Understanding,2008,110(3):346-359
|
CSCD被引
976
次
|
|
|
|
19.
Mikolajczyk K. A performance evaluation of local descriptors.
IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630
|
CSCD被引
646
次
|
|
|
|
20.
Viola P. Robust real-time face detection.
International Journal of Computer Vision,2004,57(2):137-154
|
CSCD被引
540
次
|
|
|
|
|