title: A fast and fully automatic method for cerebrovascular segmentation on time-of-flight (TOF) MRA image.
abstract: The precise three-dimensional (3-D) segmentation of cerebral vessels from magnetic resonance angiography (MRA) images is essential for the detection of cerebrovascular diseases (e.g., occlusion, aneurysm). The complex 3-D structure of cerebral vessels and the low contrast of thin vessels in MRA images make precise segmentation difficult. We present a fast, fully automatic segmentation algorithm based on statistical model analysis and improved curve evolution for extracting the 3-D cerebral vessels from a time-of-flight (TOF) MRA dataset. Cerebral vessels and other tissue (brain tissue, CSF, and bone) in TOF MRA dataset are modeled by Gaussian distribution and combination of Rayleigh with several Gaussian distributions separately. The region distribution combined with gradient information is used in edge-strength of curve evolution as one novel mode. This edge-strength function is able to determine the boundary of thin vessels with low contrast around brain tissue accurately and robustly. Moreover, a fast level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Quantitative comparisons with 10 sets of manual segmentation results showed that the average volume sensitivity, the average branch sensitivity, and average mean absolute distance error are 93.6%, 95.98%, and 0.333 mm, respectively. By applying the algorithm to 200 clinical datasets from three hospitals, it is demonstrated that the proposed algorithm can provide good quality segmentation capable of extracting a vessel with a one-voxel diameter in less than 2 min. Its accuracy and speed make this novel algorithm more suitable for a clinical computer-aided diagnosis system.
authors.affiliation: Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, Yanagido, Gifu, Japan. xgao_bj@yahoo.com.cn
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
authors.forename: Yoshikazu
authors.initials: Y
authors.lastname: Uchiyama
authors.forename: Xiangrong
authors.initials: X
authors.lastname: Zhou
authors.forename: Takeshi
authors.initials: T
authors.lastname: Hara
authors.forename: Takahiko
authors.initials: T
authors.lastname: Asano
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Fujita
mesh_terms: D000465:Algorithms; D002560:Cerebrovascular Circulation; D002561:Cerebrovascular Disorders / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity
references.citation: Med Image Anal. 2006 Jun;10(3):317-29
references.pmid: 16464631
references.citation: IEEE Trans Med Imaging. 2000 Jun;19(6):568-76
references.pmid: 11026460
references.citation: IEEE Trans Image Process. 2006 May;15(5):1171-81
references.pmid: 16671298
references.citation: IEEE Trans Med Imaging. 2000 Apr;19(4):337-46
references.pmid: 10909928
references.citation: Med Image Anal. 2002 Jun;6(2):109-28
references.pmid: 12044999
references.citation: IEEE Trans Image Process. 2001;10(8):1169-86
references.pmid: 18255534
references.citation: Acad Radiol. 1998 Apr;5(4):289-305
references.pmid: 9561263
references.citation: Acad Radiol. 2004 Apr;11(4):419-35
references.pmid: 15109014
references.citation: Med Image Comput Comput Assist Interv. 2006;9(Pt 2):799-806
references.pmid: 17354846
references.citation: Med Image Anal. 2001 Sep;5(3):173-83
references.pmid: 11524224
references.citation: Med Image Anal. 2008 Aug;12(4):497-513
references.pmid: 18375175
references.citation: J Med Eng Technol. 2008 Jan-Feb;32(1):1-9
references.pmid: 18183515
references.citation: J Magn Reson Imaging. 2005 Jun;21(6):715-25
references.pmid: 15906324
references.citation: IEEE Trans Inf Technol Biomed. 2002 Dec;6(4):338-50
references.pmid: 15224848
references.citation: IEEE Trans Image Process. 1998;7(3):433-43
references.pmid: 18276263
references.citation: Med Image Anal. 2006 Apr;10(2):200-14
references.pmid: 16263325
references.citation: IEEE Trans Med Imaging. 1996;15(6):859-70
references.pmid: 18215965
references.citation: IEEE Trans Image Process. 2001;10(7):1010-9
references.pmid: 18249674
references.citation: IEEE Trans Med Imaging. 2002 Feb;21(2):61-75
references.pmid: 11929106
references.citation: IEEE Trans Med Imaging. 1999 Oct;18(10):946-56
references.pmid: 10628954
references.citation: IEEE Trans Med Imaging. 1996;15(4):429-42
references.pmid: 18215925
references.citation: Med Image Comput Comput Assist Interv. 2005;8(Pt 1):34-42
references.pmid: 16685826
references.citation: Med Image Anal. 2006 Feb;10(1):2-18
references.pmid: 15893953
references.citation: IEEE Trans Med Imaging. 1999 Oct;18(10):938-45
references.pmid: 10628953