title: A vessel active contour model for vascular segmentation.
abstract: This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.
authors.affiliation: College of Information Science & Technology, Beijing Normal University, Beijing 100875, China.
authors.forename: Yun
authors.identifier: 0000-0001-5574-2325
authors.initials: Y
authors.lastname: Tian
authors.affiliation: Business School, Henan Normal University, Xinxiang 453007, China.
authors.forename: Qingli
authors.initials: Q
authors.lastname: Chen
authors.affiliation: Department of Obstetrics and Gynecology, Navy General Hospital, Beijing 100048, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: School of Design, Communication & Information Technology, University of Newcastle, Callaghan, NSW 2308, Australia.
authors.forename: Yu
authors.initials: Y
authors.lastname: Peng
authors.affiliation: Department of Radiology, Navy General Hospital, Beijing 100048, China.
authors.forename: Qingjun
authors.initials: Q
authors.lastname: Wang
authors.affiliation: College of Information Science & Technology, Beijing Normal University, Beijing 100875, China.
authors.forename: Fuqing
authors.identifier: 0000-0002-3849-8532
authors.initials: F
authors.lastname: Duan
authors.affiliation: College of Information Science & Technology, Beijing Normal University, Beijing 100875, China.
authors.forename: Zhongke
authors.initials: Z
authors.lastname: Wu
authors.affiliation: College of Information Science & Technology, Beijing Normal University, Beijing 100875, China.
authors.forename: Mingquan
authors.initials: M
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D006025:Glycosaminoglycans / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated
chemical_list: D006025:Glycosaminoglycans; C007858:glucuronyl glucosamine glycan sulfate
references.citation: Int J Comput Assist Radiol Surg. 2012 Mar;7(2):199-205
references.pmid: 21744244
references.citation: Med Image Anal. 2007 Jun;11(3):224-41
references.pmid: 17434784
references.citation: IEEE Trans Syst Man Cybern B Cybern. 2010 Jun;40(3):857-67
references.pmid: 19887322
references.citation: IEEE Trans Image Process. 2005 Oct;14(10):1486-502
references.pmid: 16238055
references.citation: IEEE Trans Med Imaging. 2005 Apr;24(4):486-99
references.pmid: 15822807
references.citation: IEEE Trans Image Process. 2007 Aug;16(8):1994-2004
references.pmid: 17688204
references.citation: IEEE Trans Image Process. 2008 Oct;17(10):1940-9
references.pmid: 18784040
references.citation: IEEE Trans Med Imaging. 2003 Oct;22(10):1224-34
references.pmid: 14552577
references.citation: IEEE Trans Image Process. 1998;7(3):359-69
references.pmid: 18276256
references.citation: Med Image Anal. 2009 Dec;13(6):819-45
references.pmid: 19818675
references.citation: IEEE Trans Cybern. 2014 Mar;44(3):418-28
references.pmid: 23797311
references.citation: Med Image Anal. 2006 Feb;10(1):2-18
references.pmid: 15893953
references.citation: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2007 Jul 16;2007(17-22 June 2007):1-7
references.pmid: 21311729
references.citation: IEEE Trans Biomed Eng. 2012 Feb;59(2):464-73
references.pmid: 22067226
references.citation: Med Image Anal. 2006 Jun;10(3):317-29
references.pmid: 16464631
references.citation: IEEE Trans Image Process. 2005 Oct;14(10):1512-23
references.pmid: 16238057
references.citation: Med Phys. 2005 Sep;32(9):3017-28
references.pmid: 16266116
references.citation: Med Image Anal. 2001 Sep;5(3):195-206
references.pmid: 11524226
references.citation: IEEE Trans Image Process. 2001;10(2):266-77
references.pmid: 18249617
references.citation: IEEE Trans Syst Man Cybern B Cybern. 2011 Apr;41(2):518-25
references.pmid: 20875975
references.citation: Med Image Anal. 2006 Apr;10(2):200-14
references.pmid: 16263325
references.citation: IEEE Trans Biomed Eng. 2011 Apr;58(4):1023-32
references.pmid: 21138795