Fully Automatic Location Method of Ostial Layers and Points of Coronary Arteries for Computed Tomographic Angiographic Images

doi:10.12068/j.issn.1005-3026.2020.11.008

Abstract

Abstract: Firstly， the ostial layer of the left coronary arteries by change of roundness is determined on the basis of multi-condition region growth and the anatomic structure， and the ostial point of the left coronary arteries within the specified range is determined.Secondly， the ascending aorta center and the ostial range of the right coronary arteries are calculated， and the peaks and valleys of the distance curve combined with the features of the anatomic structure are utilized to determine ostial layer and point of the right coronary arteries. The parameters of the proposed method are statistically verified. Finally， 57 subjects are tested. The accuracy of locating the ostial layers of left and right coronary arteries are 98.2% and 96.5%， respectively. The average distances between located ostial points of left and right coronary arteries and the ground truth are (1.22± 0.37)mm and (1.76± 0.49)mm， respectively. The experiments demonstrate the validity of the proposed method.

Key words: ostia of left coronary arteries, ostia of right coronary arteries, ascending aorta, region growth, distance curve

CLC Number:

TP751

LUO Yang， QI Lin， XU Li-sheng. Fully Automatic Location Method of Ostial Layers and Points of Coronary Arteries for Computed Tomographic Angiographic Images[J]. Journal of Northeastern University Natural Science, 2020, 41(11): 1570-1576.

References

[1]Global Health Estimates 2016:Deaths by cause，age，sex，by country and by region，2000-2016［R］.Geneva:World Health Organization，2018.
[2]Shams M，Salem M A M，Hamad S，et al. Coronary artery tree segmentation in computed tomography angiography using Otsu method［C］//Eighth IEEE International Conference on Intelligent Computing and Information Systems (ICICIS).Cairo，2017:416-420.
[3]Kerkeni A，Benabdallah A，Manzanera A，et al.A coronary artery segmentation method based on multiscale analysis and region growing［J］.Computerized Medical Imaging and Graphics，2016，48:49-61.
[4]Cui H F，Wang D S，Wan M，et al. Coronary artery segmentation via Hessian filter and curve-skeleton extraction［C］//IEEE Conference on Biomedical Engineering and Sciences (IECBES).Kuala Lumpur，2014:93-98.
[5]Lesage D，Angelini E D，Funka-Lea G，et al. Adaptive particle filtering for coronary artery segmentation from 3D CT angiograms［J］.Computer Vision and Image Understanding，2016，151:29-46.
[6]Shahzad R，Kiris，li H，Metz C，et al. Automatic segmentation，detection and quantification of coronary artery stenoses on CTA［J］.The International Journal of Cardiovascular Imaging，2013，29(8):1847-1859.
[7]Jawaid M M，Liatsis P，Narejo S.Automated extraction of the coronary tree by integrating localized aorta-based intensity distribution statistics in active contour segmentation［C］//IEEE International Conference on Developments of E-Systems Engineering (DeSE).Duai，2015:83-87.
[8]Ge S，Shi Z F，Peng G M，et al. Two-steps coronary artery segmentation algorithm based on improved level set model in combination with weighted shape-prior constraints［J］.Journal of Medical Systems，2019，43(7):210.
[9]Feng C L，Hu Y.Segmentation of coronary artery using region based level set with edge preservation［J］.Journal of Medical Imaging & Health Informatics，2016，6(7):1727-1731.
[10]Fathi A，Naghsh-Nilchi A R.General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images［J］.Pattern Analysis and Applications，2014，17(1):69-81.
[11]Liu L，Xu J，Liu Z.Automatic segmentation of coronary lumen based on minimum path and image fusion from cardiac computed tomography images［J］.Cluster Computing，2019，22(1):1559-1568.
[12]Chen S T，Wang T D，Lee W J，et al.Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform［J］.Biomedicine Research International，2015，2015:1-9.
[13]Jawaid M M，Rajani R，Liatsis P，et al.A hybrid energy model for region based curve evolution—application to CTA coronary segmentation［J］.Computer Methods & Programs in Biomedicine，2017，144:189-202.
[14]Gao Z F，Liu X，Qi S，et al.Automatic segmentation of coronary tree in CT angiography images［J］.International Journal of Adaptive Control & Signal Processing，2017，33:1239-124.
[15]Hennemuth A，Boskamp T，Fritz D，et al.One-click coronary tree segmentation in CT angiographic images［J］.International Congress Series，2005，1281:317-321.
[16]Wang C L，Smedby O.An automatic seeding method for coronary artery segmentation and skeletonization in CTA［J］.The Insight Journal，2008，43:1-8.
[17]Zheng Y F，Tek H，Funka-Lea G，et al. Efficient detection of native and bypass coronary ostia in cardiac CT volumes:anatomical vs.pathological structures［C］//Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011).Berlin:Springer，2011:403-410.
[18]Gong Z H，Shen Z X，Zhang D，et al.One-click detection of coronary artery ostia from three dimensional CTA data［C］//2015 IEEE International Conference on Information & Automation.Lijiang，2015:877-880.
[19]Joshi S D，Joshi S S，Athavale S A.Origins of the coronary arteries and their significance［J］.Clinics，2010，65(1):79-84.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)(上接第1569页)
[9]Lai W，Huang J，Ahuja N，et al.Deep Laplacian pyramid networks for fast and accurate super-resolution［C］//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway:IEEE，2017:5835-5843.
[10]Tai Y，Yang J，Liu X，et al.MemNet:a persistent memory network for image restoration［C］//IEEE International Conference on Computer Vision (ICCV).Piscataway:IEEE，2017:4549-4557.
[11]Tai Y，Yang J，Liu X.Image super-resolution via deep recursive residual network［C］//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE，2017:2790-2798.
[12]Zhang K，Zuo W，Zhang L.Learning a single convolutional super-resolution network for multiple degradation［C］//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE，2018:3262-3271.
[13]Li J，Fang F，Mei K，et al.Multi-scale residual network for image super-resolution［C］//European Conference on Computer Vision(ECCV).Berlin:Springer-Verlag，2018:527-542.
[14]Timofte R，Agustsson E.NTIRE 2017 challenge on single image super-resolution:dataset and study［C］//IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Piscataway:IEEE，2017:1122-1131.
[15]Shi W，Caballero J，Huszar F，et al.Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network［C］//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE，2016:1874-1883.
[16]Szegedy C，Liu W，Jia Y，et al.Going deeper with convolutions［C］//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway:IEEE，2015:1-9.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)