A novel vessel-constrained active contour with application to vessel segmentation

Si LIANG; Lei WANG; Xiao-dong YANG

doi:10.3788/YJYXS20163107.0686

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A novel vessel-constrained active contour with application to vessel segmentation

Display and Image | 更新时间：2020-07-07

- A novel vessel-constrained active contour with application to vessel segmentation
- Chinese Journal of Liquid Crystals and Displays Vol. 31, Issue 7, Pages: 686-694(2016)
- 作者机构：
  
  1.中国科学院苏州生物医学工程技术研究所, 江苏苏州 21215163
  2.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  3.中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20163107.0686
  CLC： TP391
- Received：08 September 2015，
  
  Accepted：25 November 2015，
  
  Published：2016-07
- 稿件说明：
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Si LIANG, Lei WANG, Xiao-dong YANG. A novel vessel-constrained active contour with application to vessel segmentation[J]. Chinese journal of liquid crystals and displays, 2016, 31(7): 686-694.
DOI：

Si LIANG, Lei WANG, Xiao-dong YANG. A novel vessel-constrained active contour with application to vessel segmentation[J]. Chinese journal of liquid crystals and displays, 2016, 31(7): 686-694. DOI： 10.3788/YJYXS20163107.0686.

摘要

活动轮廓作为一种重要的图像分割工具，近几年来在理论和应用方面都有很大的发展。然而，现有轮廓模型在处理灰度均匀性较差的图像时，通常存在较高的分割误差，并且对初始轮廓曲线位置敏感。为此，本文提出一种基于血管特征约束的活动轮廓模型，该模型首先使用局部相位（Local Phase）的血管增强算法对图像进行增强处理以生成一种不同于图像灰度的血管特征信息，然后将血管信息和图像灰度以线性加权的形式引入到局部二值拟合（Local Binary Fitting

LBF）能量泛函中，指导图像血管分割。基于视网膜血管图像数据（Digital Retinal Images for Vessel Extraction，DRIV）的实验显示：该模型能成功地从灰度分布不均匀和弱边界轮廓的视网膜图像中提取血管，分割灵敏度和准确性分别达到74.43%和93.67%，同时对初始轮廓曲线位置的敏感性大为降低。由上述可知，该模型具有高分割准确性和低初始位置敏感性。

Abstract

Active Contour Models are the essential instruments for image segmentation and have great development in both theory and application. However

these existing models cannot work well in the presence of intensity heterogeneity

and are in general sensitive to initial curve places. Therefore

a novel active contour is proposed so as to extract correctly vessels

which takes into account image intensity and vessel features simultaneously. The new model is obtained by local phase vesselness-enhanced algorithm

and is based on local binary fitting. Experimental results

which based on publicly available Digital Retinal Images for Vessel Extraction (DRIVE)

show that our model can successfully extract the desired vessels in the presence of intensity inhomogeneity with 74.43% pixel sensitivity and 93.67% segmentation accuracy

and the model is insensitive to initial curve placement. This demonstrates our model is competent for image segmentation with high accuracy and robustness.

关键词

Keywords

references

PARAGIOS N,DERICHE R.Geodesic active contours and level sets for the detection and tracking of moving objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence ,2000,22(3):266-280.

MIKIC I,KRUCINSKI S,THOMAS J D.Segmentation and tracking in echocardiographic sequences:Active contours guided by optical flow estimates[J]. IEEE transactions on medical imaging, 1998,17(2):274-284.

GUIDRY D L,FARAG A A.Active Contours:An overview with applications to motion artifact cancellation in MRI[C]. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Hong Kong,China:IEEE,1998,2:644-647.

MYLONA E A,SAVELONAS M A,MAROULIS D.Automated adjustment of region-based active contour parameters using local image geometry[J]. IEEE Transactions on Cybernetics ,2014,44(12):2757-2770.

SETHIAN J A.A review of level set and fast marching methods for image processing[M]//BOURLIOUX A,GANDER M J,SABIDUSSI G. Modern Methods in Scientific Computing and Applications. Netherlands:Springer,2002,75:365-396.

ESTELLERS V,ZOSSO D,LAI R J, et al. Efficient algorithm for level set method preserving distance function[J]. IEEE Transactions on Image Processing ,2012,21(12):4722-4734.

DONG B,CHIEN A,MAO Y, et al. Level set based surface capturing in 3D medical images[M]//METAXAS D,AXEL L,FICHTINGER G, et al. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2008.Berlin Heidelberg:Springer,2008,5241:162-169.

KASS M,WITKIN A,TERZOPOULOS D.Snakes:Active contour models[J]. International Journal of Computer Vision ,1988,1(4):321-331.

CASELLES V,CATTÉ F,COLL T, et al. A geometric model for active contours in image processing[J]. Numerische Mathematik, 1993,66(1):1-31.

LI C M,XU C Y,GUI C F, et al. Level set evolution without re-initialization:A new variational formulation[C]. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition ,San Diego,CA,USA:IEEE,2005,1:430-436.

LI C M,LIU J D,FOX M D.Segmentation of edge preserving gradient vector flow:An approach toward automatically initializing and splitting of snakes[C]. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition ,San Diego,CA,USA:IEEE,2005,1:162-167.

VESE L A,CHAN T F.A multiphase level set framework for image segmentation using the Mumford and Shah model[J]. International Journal of Computer Vision ,2002,50(3):271-293.

CHAN T F,VESE L A.Active contours without edges[J]. IEEE Transactions on Image Processing, 2001,10(2):266-277.

ROUSSON M,PARAGIOS N,DERICHE R.Implicit active shape models for 3D segmentation in MR imaging[M]//BARILLOT C,HAYNOR D R,HELLIER P. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2004.Berlin Heidelberg:Springer,2004,3216:209-216.

CASELLES V,KIMMEL R,SAPIRO G.Geodesic active contours[J].International Journal of Computer Vision,1997,22(1):61-79.

LI C M,KAO C Y,GORE J C, et al. Implicit active contours driven by local binary fitting energy[C]. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition ,Minneapolis,MN:IEEE,2007:1-7.

WU H,APPIA V,YEZZI A.Numerical conditioning problems and solutions for nonparametric i.i.d.statistical active contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence ,2013,35(6):1298-1311.

AN J,ROUSSON M,XU C Y.Γ-convergence approximation to piecewise smooth medical image segmentation[M]//AYACHE N,OURSELIN S,MAEDER A. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2007.Berlin Heidelberg:Springer,2007,4792:495-502. http://cn.bing.com/academic/profile?id=1808344143&encoded=0&v=paper_preview&mkt=zh-cn

LANKTON S,NAIN D,YEZZI A, et al. Hybrid geodesic region-based curve evolutions for image segmentation[C]. Proceedings of the Medical Imaging 2007:Physics of Medical Imaging ,San Diego,CA:SPIE,2007,6510:65104U.

YEZZI A,TSAI A,WILLSKY A.Medical image segmentation via coupled curve evolution equations with global constraints[C]. Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis ,Hilton Head Island,SC:IEEE,2000:12-19.

KIM J,FISHER J W,CETIN M, et al. Incorporating complex statistical information in active contour-based image segmentation[C]. Proceedings of the 2003 International Conference on Image Processing ,Barcelona,Spain:IEEE,2003,2:Ⅱ-655-8.

LI C M,KAO C Y,GORE J C, et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE Transactions on Image Processing ,2008,17(10):1940-1949.

LANKTON S,TANNENBAUM A.Localizing region-based active contours[J]. IEEE Transactions on Image Processing ,2008,17(11):2029-2039.

张艳超,赵建.改进C-V分割算法在多光谱成像仪中的应用[J].中国光学,2015,8(1):68-73.

ZHANG Y C,ZHAO J.Application of improved C-V segmentation in multi-spectral imager[J]. Chinese Optics, 2015,8(1):68-73.(in Chinese)

LÄTHÉN G,JONASSON J,BORGA M.Blood vessel segmentation using multi-scale quadrature filtering[J]. Pattern Recognition Letters ,2010,31(8):762-767.

WANG L,HE L,MISHRA A, et al. Active contours driven by local Gaussian distribution fitting energy[J]. Signal Processing ,2009,89(12):2435-2447.

WANG L,LI C M,SUN Q S, et al. Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation[J]. Computerized Medical Imaging and Graphics ,2009,33(7):520-531.

杨名宇,李刚.利用区域信息的航拍图像分割[J].中国光学,2014,7(5):779-785.

YANG M Y,LI G.Aerial image segmentation with region information[J]. Chinese Optics ,2014,7(5):779-785.(in Chinese)

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