3D vascular tree segmentation using a multiscale vesselness function and a level set approach

• Autorzy: Woźniak T. , Strzelecki M. , Majos A. , Stefańczyk L.

Identyfikatory

DOI

10.1016/j.bbe.2016.11.003

• Języki publikacji: EN

Abstrakty

EN

The paper presents a method aimed at segmentation of a vascular network in 3D medical data. The method implements an extended version of a vesselness function that considers multiscale image filtering to emphasize vessels of different diameters. This function is combined with a level set approach based on a Chan–Vese model. The proposed method was evaluated on medical images of the brain and hand vasculature. These images were obtained by different modalities, including angio-CT and two MR acquisition protocols. The proposed technique was quantitatively validated for the tree phantom image by assessing segmentation accuracy and for the angio-CT images by estimating diameters of vessel fragments. Two radiologists provided also qualitative evaluation of this approach. It was demonstrated that this method ensures correct segmentation of a vessel tree in the analyzed images. Moreover, it enables detection of thinner vessel branches when compared to single scale vesselness function approaches.

Słowa kluczowe

EN

3D vascular network; magnetic resonance; computed tomography angiography; level set segmentation

PL

dane 3D; sieć naczyń krwionośnych; rezonans magnetyczny; angiografia tomografii komputerowej; segmentacja obrazu

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 1
• Strony: 66--77
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wykr.

Twórcy

autor

Woźniak T.

• Institute of Electronics, Lodz University of Technology, Wólczańska 211/215, 90-924 Łódź, Poland

autor

Strzelecki M.

• Institute of Electronics, Lodz University of Technology, Wólczańska 211/215, 90-924 Łódź, Poland

autor

Majos A.

• Department of Radiological and Isotopic Diagnostics and Therapy, Medical University of Lodz, Łódź, Poland

autor

Stefańczyk L.

• Department of Diagnostic Imaging, Medical University of Lodz, Łódź, Poland

Bibliografia

• [1] Kirbas C, Quek F. A review of vessel extraction techniques and algorithms. ACM Comput Surv 2004;36(2):81–121.
• [2] Lesage D, Angelini ED, Bloch I, Funka-Lea G. A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 2009;(13):819–45.
• [3] Bullitt E, Reardon DA, Smith JK. A review of micro- and macrovascular analyses in the assessment of tumor- associated vasculature as visualized by MR. Neuroimage 2007;(37):116–9.
• [4] Materka A, Kociński M, Blumenfeld J, Klepaczko A, Deistung A, Serres B, et al. Automated modeling of tubular blood vessels in 3D MR angiography images. Proc. 9th International Symposium on Image and Signal Processing and Analysis. 2015. pp. 54–9.
• [5] Tian Y, Chen Q, Wang W, Peng Y, Wang Q, Duan F, et al. A vessel active contour model for vascular segmentation. BioMed Res Int 2014. http://dx.doi.org/10.1155/2014/106490.
• [6] Shang Y, Deklerck R, Nyssen E, Markova A, de Mey J, Yang X, et al. Vascular active contour for vessel tree segmentation. IEEE Trans Biomed Eng 2011;58(4):1023–32.
• [7] Dehkordi MT, Doost Hoseini AM, Sadri S, Soltanianzadeh H. Local feature fitting active contour for segmenting vessels in angiograms. IET Comput Vis 2014;8(3):161–70.
• [8] Freiman M, Joskowicz L, Broide N, Natanzon M, Nammer E, Shilon O, et al. Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation. Int J Comput Assist Radiol Surg 2012;(7):799–812.
• [9] Hernandez-Vela A, Gatta C, Escalera S, Igual L, Martin-Yuste V, Sabate M, et al. Accurate coronary centerline extraction, caliber estimation, and catheter detection in angiographies. IEEE Trans Inf Technol Biomed 2012;16(6):1332–40.
• [10] Zhao S, Zhou M, Tian Y, Xu P, Wu Z, Deng Q. Extraction of vessel networks based on multiview projection and phase field model. Neurocomputing 2015;(162):234–44.
• [11] Hong Q, Li Q, Wang B, Li Y, Yao J, Liu K, et al. 3D vasculature segmentation using localized hybrid level-set method. BioMed Eng Online 2014;13(169). http://dx.doi.org/10.1186/1475-925X-13-169.
• [12] Jin J, Yang L, Zhang X, Ding M. Vascular tree segmentation in medical images using Hessian-based multiscale filtering and level set method. Comput Math Methods Med 2013. http://dx.doi.org/10.1155/2013/502013.
• [13] Gao X, Uchiyama Y, Zhou X, Hara T, Asano T, Fujita H. A fast and fully automatic method for cerebrovascular segmentation on time-of-flight (TOF) MRA image. J Digit Imaging 2011;24(4):609–25.
• [14] Frangi AF, Niessen WJ, Nederkoorn PJ, Bakker J, Mali WP, Viergever MA. Quantitative analysis of vascular morphology from 3D MR angiograms: in vitro and in vivo results. Magn Reson Med 2001;45:311–22.
• [15] Jinzhu Y, Shuang M, Qi S, Wenjun T, Mengjia X, Nan C, et al. Improved Hessian multiscale enhancement filter. Bio-Med Mater Eng 2014;(24):3267–75.
• [16] Forkert ND, Schmidt-Richberg A, Fiehler J, Illies T, Moller D, Saring D, et al. 3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights. Magn Reson Imaging 2013;(31):262–71.
• [17] Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process 2001;10(2):266–77.
• [18] Kociński M, Klepaczko A, Materka A, Chekenya M, Lundervold A. 3D image texture analysis of simulated and real-world vascular trees. Comput Methods Progr Biomed 2012;107(2):140–54.
• [19] Manniesing R, Velthuis BK, van Leeuwen MS, van der Schaaf IC, van Laar PJ, Niessen WJ. Level set based cerebral vasculature segmentation and diameter quantification in CT angiography. Med Image Anal 2006;10:200–14.
• [20] Parker D, Yuan C, Blatter D. MR angiography by multiple thin slab 3D acquisition. Magn Reson Med 1991;17(2):434– 51.
• [21] Materka A, Strzelecki M. On the importance of MRI nonuniformity correction for texture analysis. Proc. of IEEE SPA. 2013. pp. 118–23.
• [22] Kholmovski EG, Alexander AL, Parker DL. Correction of slab boundary artifact using histogram matching. J Magn Reson Imaging 2002;15(5):610–7.
• [23] Jain A. Fundamentals of digital image processing. Prentice Hall: Englewood Cliffs; 1989.
• [24] Strzelecki M, Woźniak T, Olszycki M, Szymczyk K, Stefańczyk L. Analysis of the Hand's small vessels based on MR angiography and level-set approach. Proc. of International Conference of Computer Vision and Graphics. LNCS 8671. 2014. pp. 618–25.
• [25] Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, et al. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 1998;2(2):143–68.
• [26] Woźniak T, Strzelecki M. Segmentation of 3D magnetic resonance brain vessel images based on level set approaches. Proc. of IEEE SPA. 2015. pp. 56–61.
• [27] Tan P, Steinbach M, Kumar V. Introduction to data mining. Addison Wesley; 2005.
• [28] Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion- Robin J-C, Pujol S, et al. 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 2012;30(9):1323–41.
• [29] Muschelli J, Ullman NL, Mould WA, Vespa P, Hanley DF, Crainiceanu CM. Validated automatic brain extraction of head CT images. Neuroimage 2015;114:379–85.
• [30] Kalavathi P, Prasath VBS. Methods on skull stripping of MRI head scan images – a review. J Digit Imaging 2016;29(3). http://dx.doi.org/10.1007/s10278-015-9847-8.
• [31] Forkert ND, Saring D, Fiehler J, Illies T, Moller D, Handels H. Automatic brain segmentation in time-of-flight MRA images. Methods Inf Med 2009;48(5):399–407.
• [32] Klepaczko A, Szczypiński P, Dwojakowski G, Strzelecki M, Materka A. Computer simulation of magnetic resonance angiography imaging: model description and validation. PLoS ONE 2014;9(4). http://dx.doi.org/10.1371/journal.pone.0093689.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).