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Received：27 May 2019，
Revised：2019-7-29，
Accepted：06 August 2019，
Published：16 February 2020
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目的
2
血管内超声（IVUS）图像动脉壁边界分割不仅对血管壁和斑块特征的定量分析至关重要，而且对血管弹性定性分析和重建动脉3维模型也是必需的。针对IVUS图像传统分割方法建模复杂、运算量大且需分别设计算法串行提取内膜和外膜的缺点，本文提出基于极值区域检测的IVUS图像并行分割方法。
方法
2
本文方法包含极值区域检测、极值区域筛选以及轮廓拟合3部分。对单帧IVUS图像提取极值区域，经面积筛选后得到候选区域，并将区域的局部二值模式（LBP）特征、灰度差异和边缘周长的乘积作为筛选矢量在候选区域中提取代表管腔和介质的两个极值区域，并进行轮廓的椭圆拟合化，完成分割。
结果
2
在包含326幅20 MHz的IVUS（intravascular ultrasound）B模式图像的标准公开数据集上，定性展示极值区域轮廓和椭圆拟合轮廓，并与专家手动绘制的结果进行对比；然后使用DC（dice coefficient）、JI（jaccard index）、PAD（percentage of area difference）指标以及HD（hausdorff distance）对本文算法做鲁棒性测试和泛化测试，实验中内膜各指标值分别为0.94±0.02，0.90±0.04，0.05±0.05，0.28±0.14 mm，外膜各指标值分别为0.91±0.07，0.87±0.11，0.11±0.11，0.41±0.31 mm，与相关文献的定量对比实验结果表明本文算法提取的内外膜性能均有所提高。此外，本文方法在临床数据集上的测试效果也很好，与专家手动描绘十分接近。
结论
2
结合极值区域检测的IVUS图像并行分割，算法在精度和鲁棒性方面均得到了改善。
Objective
2
Intravascular ultrasound (IVUS) image segmentation of arterial wall boundaries are essential not only for the quantitative analysis of the characteristics of vascular walls and plaques but also for the qualitative analysis of vascular elasticity and the reconstruction of the 3D model of arteries. The importance lies in the following:1)IVUS image segmentation is the basis for follow-up work
such as plaque extraction and recognition
vessel wall elasticity analysis
and image registration. 2) Doctors must evaluate the morphological characteristics of blood vessels and plaques
such as the maximum or minimum diameter of the lumen
cross-sectional area
and plaque area. IVUS provides a reliable data support for doctors to diagnose patients objectively.3) IVUS can locate the region of interest to determine the position and shape of the anatomical structure for interventional surgery and the diagnosis and treatment targets for radiotherapy
chemotherapy
and surgery. However
given the different environments in which the intima and adventitia are located
traditional segmentation methods
which belong to serial extraction methods
need to design the segmentation algorithms for intima and adventitia separately. Moreover
extremely complex models affect the speed of segmentation. To address these problems
this paper proposes a segmentation method based on the extreme region detection of IVUS images.
Method
2
The problem of edge detection is broadened to the problem of extreme region detection
and the proposed method consists of three parts:extreme region detection
extreme region screening
and contour fitting. First
edge points are extracted from the IVUS image
and a global vector is created by using the edge points and threshold images by each gray level to obtain the gray thresholds. The obtained thresholds make the change of threshold images most stable.Next
the final threshold images are obtained on the basis of the filtered gray thresholds. The morphological closing operation is used to fill in the small holes of the threshold images
and the connected component labeling algorithm is used to mark the connected regions in the threshold images to obtain the final extreme regions. In addition
extreme regions contains regions with unstable states and large or small areas that cannot represent the lumen and media because the extracted extreme regions contain many sub-regions. Therefore
the area of the extreme regions must be screened for preliminary filtering.By using local binary mode feature
gray difference
and edge circumference
a filter vector based on region stability is designed to extract two extreme regions representing the lumen and media. Finally
the contours of the lumen and media regions are fitted by ellipse to complete the segmentation.
Result
2
Qualitative and quantitative analyses are used to evaluate the accuracy of the proposed method.The extreme region and final contours are initially qualitatively displayed on a standard published dataset containing 32 620 MHz IVUS B-mode images. The extracted final contours are qualitatively compared with the results drawn manually by clinical experts.The artifacts are also classified on the basis of their types. For images without artifacts and with different types of artifacts
the robust performance and generalization performance of the proposed algorithm are verified by calculating the DC coefficient
JI index
PAD index
and HD distance. On the basis of the DC coefficient
JI index
PAD index
and HD distance
the inner border index values are 0.94±0.02
0.90±0.04
0.05±0.05
and 0.28±0.14 mm
respectively; the outer border index values are 0.91±0.07
0.87±0.11
0.11±0.11
and 0.41±0.31 mm
respectively.In addition
the values of DC coefficient
JI index
PAD index
and HD distance of the IVUS image segmentation algorithms in the relevant literature in the last few years are compared with those of the proposed method.The quantitative comparison with the relevant literature in the last few years shows the improved performance of the inner and outer borders extracted by the proposed method. In addition
the test results of the proposed method on the clinical dataset are very good.
Conclusion
2
The method proposed is suitable for the extraction of not only the inner border but also the outer border; it is a parallel extraction algorithm. Experiment results show that in addition to high extraction accuracy
the proposed method has strong robustness and outperforms several state-of-the-art segmentation approaches.
Ahonen T, Hadid A and Pietikäinen M. 2004. Face recognition with local binary patterns//Proceedings of the 8th European Conference on Computer Vision. Prague, Czech Republic: Springer: 469-481[ DOI: 10.1007/978-3-540-24670-1_36 http://dx.doi.org/10.1007/978-3-540-24670-1_36 ]
Balocco S, Gatta C, Ciompi F, Wahle A, Radeva P, Carlier S, Unal G, Sanidas E, Mauri J, Carillo X, Kovarnik T, Wang CW, Chen HC, ExarchosTP, Fotiadis DI, Destrempes F, Cloutier G, Pujol O, Alberti M, Mendizabal-Ruiz EG, Rivera M, Aksoy T, Downe RW and Kakadiaris IA. 2014. Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Computerized Medical Imaging and Graphics, 38(2):70-90[DOI:10.1016/j.compmedimag.2013.07.001]
Ciompi F, Pujol O, Gatta C, Alberti M, Balocco S, Carrillo X, Mauri-Ferre J and Radeva P. 2012. HoliMAB:a holistic approach for media-adventitia border detection in intravascular ultrasound. Medical Image Analysis, 16(6):1085-1100[DOI:10.1016/j.media.2012.06.008]
Fang T. 2018. Research of Level Set Segmentation Models and Their Applications in Medical Images. Chengdu: Southwest Jiaotong University http://cdmd.cnki.com.cn/Article/CDMD-10613-1018709090.htm .
方婷. 2018.水平集分割模型及其在医学图像上的应用研究.成都: 西南交通大学
Faraji M, Shanbehzadeh J, Nasrollahi K and Moeslund T B. 2015. Extremal regions detection guided by maxima of gradient magnitude. IEEE Transactions on Image Processing, 24(12):5401-5415[DOI:10.1109/TIP.2015.2477215]
Frostegård J. 2005.SLE, atherosclerosis and cardiovascular disease. Journal of Internal Medicine, 257(6):485-495[DOI:10.1111/j.1365-2796.2005.01502.x]
Gao C Y. 2010.Research on Edge Extraction of Intravascular Ultrasound Sequential Image Based on Active Contour Model. Xi'an: Xi'an University of Science and Technology http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&filename=2011015201.nh .
高重阳. 2010.基于活动轮廓模型的血管内超声序列图像边缘提取研究.西安: 西安科技大学
He L F, Ren X W, Gao Q H, Zhao X, Yao B and Chao Y Y. 2017.The connected-component labeling problem:a review of state-of-the-art algorithms. Pattern Recognition, 70:25-43[DOI:10.1016/j.patcog.2017.04.018]
Hossain M M, AlMuhanna K, Zhao L M, Lal B and Sikdar S. 2013. Semiautomatic segmentation of atherosclerotic carotid artery lumen using 3D ultrasound imaging//Proceedings of SPIE 8669, MedicalImaging 2013: Image Processing.Florida, United States: SPIE: 86694A[ DOI: 10.1117/12.2007030 http://dx.doi.org/10.1117/12.2007030 ]
Jodas D S, Pereira A S and Tavares J M R S. 2017. Automatic segmentation of the lumen region in intravascular images of the coronary artery. Medical Image Analysis, 40:60-79[DOI:10.1016/j.media.2017.06.006]
Klein D A and Frintrop S. 2011. Center-surround divergence of feature statistics for salient object detection//Proceedings of 2011 International Conference on Computer Vision. Barcelona, Spain: IEEE: 2214-2219[ DOI: 10.1109/ICCV.2011.6126499 http://dx.doi.org/10.1109/ICCV.2011.6126499 ]
Lazrag H and Naceur M S. 2012. Combination of the level-set methods with the contourlet transform for the segmentation of the IVUS images. Journal of Biomedical Imaging, 2012:439597[DOI:10.1155/2012/439597]
Lin M D, Yang F, Liang S J, Zhao H S, Huang Z and Cui K. 2016. External elastic membrane border detection based on sequential learning and prior shape information for intravascular ultrasound images. Journal of Image and Graphics, 21(5):646-656
林慕丹, 杨丰, 梁淑君, 赵海升, 黄铮, 崔凯. 2016.结合先验形状信息和序贯学习的心血管内超声外弹力膜检测.中国图象图形学报, 21(5):646-656[DOI:10.11834/jig.20160512]
Majtey A P, Lamberti P W and Prato D P. 2005. Jensen-Shannon divergence as a measure of distinguishability between mixed quantum states. Physical Review A, 72(5):052310[DOI:10.1103/PhysRevA.72.052310]
Mendizabal-Ruiz E G and Kakadiaris I A. 2012. Probabilistic segmentation of the lumen from intravascular ultrasound radio frequency data//Proceedings of the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention.Nice, France: Springer: 454-461[ DOI: 10.1007/978-3-642-33418-4_56 http://dx.doi.org/10.1007/978-3-642-33418-4_56 ]
Mendizabal-Ruiz E G, Rivera M and Kakadiaris I A. 2013. Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach. Medical Image Analysis, 17(6):649-670[DOI:10.1016/j.media.2013.02.003]
Mendizabal-Ruiz G, Rivera M and Kakadiaris I A. 2008. A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA: IEEE: 1-8[ DOI: 10.1109/CVPR.2008.4587484 http://dx.doi.org/10.1109/CVPR.2008.4587484 ]
Nissen S E and Yock P. 2001. Intravascular ultrasound:novel pathophysiological insights and current clinical applications. Circulation, 103(4):604-616[DOI:10.1161/01.CIR.103.4.604]
Ojala T, Pietikainen M and Harwood D. 1994. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions//Proceedings of 12th International Conference on Pattern Recognition. Jerusalem, Israel: IEEE: 582-585[ DOI: 10.1109/ICPR.1994.576366 http://dx.doi.org/10.1109/ICPR.1994.576366 ]
Sofian H, Ming J T C and Noor N M. 2015. Detection of the lumen boundary in the coronary artery disease//Proceedings of 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). Dhaka, Bangladesh: IEEE: 143-146[ DOI: 10.1109/WIECON-ECE.2015.7443882 http://dx.doi.org/10.1109/WIECON-ECE.2015.7443882 ]
Su S R, Gao Z F, Zhang H Y, Lin Q, Hau W K and Li S. 2017. Detection of lumen and media-adventitia borders in IVUS images using sparse auto-encoder neural network//The 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, Australia: IEEE: 1120-1124[ DOI: 10.1109/ISBI.2017.7950713 http://dx.doi.org/10.1109/ISBI.2017.7950713 ]
Szpak Z L, Chojnacki W and Van Den Hengel A. 2012. A comparison of ellipse fitting methods and implications for multiple-view geometry estimation//Proceedings of 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA). Fremantle, WA, Australia: IEEE: 1-8[ DOI: 10.1109/DICTA.2012.6411722 http://dx.doi.org/10.1109/DICTA.2012.6411722 ]
Tabassum A and Dhondse S A. 2015. Text detection using MSER and stroke width transform//Proceedings of the 5th International Conference on Communication Systems and Network Technologies. Gwalior, India: IEEE: 568-571[ DOI: 10.1109/CSNT.2015.154 http://dx.doi.org/10.1109/CSNT.2015.154 ]
Vykoukal D, Chinnadurai P and Davies M G. 2014. Cardiovascular imaging, navigation and intervention: hybrid imaging and therapeutics//Garbey M, Bass B L, Berceli S, Collet C andCerveri P, eds.Computational Surgery and Dual Training. New York: Springer: 125-148.
Yan C G, Xie H T, Liu S, Yin J, Zhang Y D and Dai Q H. 2018. Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Transactions on Intelligent Transportation Systems, 19(1):220-229[DOI:10.1109/TITS.2017.2749977]
Yang J, Tong L, Faraji M and Basu A. 2018. IVUS-Net: an intravascular ultrasound segmentation network//Proceedings of the 1st International Conference on Smart Multimedia. Toulon, France: Springer: 367-377[ DOI: 10.1007/978-3-030-04375-9_31 http://dx.doi.org/10.1007/978-3-030-04375-9_31 ]
Yuan S F, Yang F, Liu S J, Ji F and Huang J. 2018a. Media-adventitia border detection based on local shape structure classification for intravascular ultrasound images. Acta Electronica Sinica, 46(7):1601-1608
袁绍锋, 杨丰, 刘树杰, 季飞, 黄靖. 2018a.基于局部形状结构分类的心血管内超声图像中-外膜边界检测.电子学报, 46(7):1601-1608[DOI:10.3969/j.issn.0372-2112.2018.07.009]
Yuan S F, Yang F, Xu L, Liu S J, Ji F and Huang J. 2018b. Lumen and media-adventitia borders detection in IVUS images using deep fully convolutional networks. Journal of Image and Graphics, 23(9):1335-1348
袁绍锋, 杨丰, 徐琳, 刘树杰, 季飞, 黄靖. 2018b.深度全卷积网络的IVUS图像内膜与中-外膜边界检测.中国图象图形学报, 23(9):1335-1348[DOI:10.11834/jig.170598]
Yuan S F, Yang F, Xu L, Wu Y Y, Huang J and Liu Y Q. 2019. Lumen and media-adventitia borders detection in IVUS images using conditional generative adversarial networks. Chinese Journal of Biomedical Engineering, 38(2):146-156
袁绍锋, 杨丰, 徐琳, 吴洋洋, 黄靖, 刘娅琴. 2019.有条件生成对抗网络的IVUS图像内膜与中-外膜边界检测.中国生物医学工程学报, 38(2):146-156[DOI:10.3969/j.issn.0258-8021.2019.02.003]
Zhu X J, Zhang P F, Shao J H, Cheng Y Z, Zhang Y and Bai J. 2011. A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation. Ultrasonics, 51(2):181-189[DOI:10.1016/j.ultras.2010.08.001]
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