Informatica logo


Login Register

  1. Home
  2. Issues
  3. Volume 32, Issue 1 (2021)
  4. Hybrid Vessel Extraction Method Based on ...

Informatica

Information Submit your article For Referees Help ATTENTION!
  • Article info
  • Full article
  • Cited by
  • More
    Article info Full article Cited by

Hybrid Vessel Extraction Method Based on Tight-Frame and EM Algorithms by Using 2D Dual Tree Complex Wavelet
Volume 32, Issue 1 (2021), pp. 1–22
Farid Abdollahi   Mehrdad Lakestani   Mohsen Razzaghi  

Authors

 
Placeholder
https://doi.org/10.15388/20-INFOR435
Pub. online: 11 February 2021      Type: Research Article      Open accessOpen Access

Received
1 September 2019
Accepted
1 November 2020
Published
11 February 2021

Abstract

The vessel extraction is very important for the vascular disease diagnosis and grading of the stenoses and aneurysms in vessels. This aids in brain surgery and making angioplasty. The presence of noise in the MRA image, etc., turns the vessel extraction into a difficult problem. In this paper, we derive a vessel extraction algorithm based on TFA and EMS algorithms. We prove the convergence of the proposed method within a few iterations. Results of applying the presented method on real 2D MRA images demonstrate that our method is very efficient.

References

 
Aghazadeh, N., Cigaroudy, L.S. (2014). A multistep segmentation algorithm for vessel extraction in medical imaging. arXiv:1412.8656.
 
Arivazhagan, S., Ganesan, L. (2003). Texture segmentation using wavelet transform. Pattern Recognition Letters, 24(16), 3197–3203.
 
Budak, Ü., Cömert, Z., Çıbuk, M., Şengür, A. (2020). DCCMED-Net: densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images. Medical Hypotheses, 134, 109426.
 
Cai, J.-F., Chan, R., Shen, L., Shen, Z. (2008). Restoration of chopped and nodded images by framelets. SIAM Journal on Scientific Computing, 30(3), 1205–1227.
 
Cai, X., Chan, R., Morigi, S., Sgallari, F. (2013). Vessel segmentation in medical imaging using a tight-frame–based algorithm. SIAM Journal on Imaging Sciences, 6(1), 464–486.
 
Candès, E., Demanet, L., Donoho, D., Ying, L. (2006). Fast discrete curvelet transforms. Multiscal e Modeling & Simulation, 5(3), 861–899.
 
Caselles, V., Catté, F., Coll, T., Dibos, F. (1993). A geometric model for active contours in image processing. Numerische mathematik, 66(1), 1–31.
 
Chan, R.H., Chan, T.F., Shen, L., Shen, Z. (2003). Wavelet algorithms for high-resolution image reconstruction. SIAM Journal on Scientific Computing, 24(4), 1408–1432.
 
Chan, T.F., Vese, L.A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266–277.
 
Cigaroudy, L.S., Aghazadeh, N. (2017). A multiphase segmentation method based on binary segmentation method for Gaussian noisy image. Signal, Image and Video Processing, 11(5), 825–831.
 
Cline, H.E. (2000). Enhanced visualization of weak image sources in the vicinity of dominant sources. Google Patents. US Patent 6,058,218.
 
Daubechies, I., Bates, B.J. (1993). Ten lectures on wavelets. The Journal of the Acoustical Society of America, 93(3), 1671.
 
Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22.
 
Do, M.N., Vetterli, M. (2005). The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 14(12), 2091–2106.
 
Dougherty, G. (2011). Medical Image Processing. Techniques and Applications. Springer Science & Business Media.
 
Duffin, R.J., Schaeffer, A.C. (1952). A class of nonharmonic Fourier series. Transactions of the American Mathematical Society, 72(2), 341–366.
 
Fajardo, V.A., Liang, J. (2017). On the EM-Tau algorithm: a new EM-style algorithm with partial E-steps. arXiv:1711.07814.
 
Firoiu, I. (2010). Complex Wavelet Transform. Application to Denoising Universitatea Politechnica, Timisoara.
 
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A. (1998). Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer-Assisted InterveNTION, Springer, pp. 130–137.
 
Gerig, G., Kikinis, R., Jolesz, F.A. (1990). Image processing of routine spin-echo MR images to enhance vascular structures: comparison with MR angiography. In: 3D Imaging in Medicine. Springer, pp. 121–132.
 
Guo, K., Labate, D. (2007). Optimally sparse multidimensional representation using shearlets. SIAM Journal on Mathematical Analysis, 39(1), 298–318.
 
Hashemzadeh, M., Azar, B.A. (2019). Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods. Artificial Intelligence in Medicine, 95, 1–15.
 
Iwasaki, T., Ritman, E.L., Fiksen-Olsen, M.J., Romero, J.C., Knox, F.G. (1985). Renal cortical perfusion-preliminary experience with the dynamic spatial reconstructor (DSR). Annals of Biomedical Engineering, 13(3–4), 259–271.
 
Kakileti, S.T., Venkataramani, K. (2016). Automated blood vessel extraction in two-dimensional breast thermography. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 380–384.
 
Kingsbury, N. (2001). Complex wavelets for shift invariant analysis and filtering of signals. Applied and Computational Harmonic Analysis, 10(3), 234–253.
 
Kirbas, C., Quek, F. (2004). A review of vessel extraction techniques and algorithms. ACM Computing Surveys (CSUR), 36(2), 81–121.
 
Krissian, K., Malandain, G., Ayache, N. (1997). Directional anisotropic diffusion applied to segmentation of vessels in 3D images. In: International Conference on Scale-Space Theories in Computer Vision, Springer, pp. 345–348.
 
Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y. (1998). Model based multiscale detection of 3D vessels. In: Proceedings. Workshop on Biomedical Image Analysis (Cat. No. 98EX162). IEEE, pp. 202–210
 
Kutyniok, G., Labate, D. (2012). Shearlets: Multiscale Analysis for Multivariate Data. Springer Science & Business Media.
 
Labate, D., Lim, W.-Q., Kutyniok, G., Weiss, G. (2005). Sparse multidimensional representation using shearlets. In: Proceedings of SPIE 5914, Wavelets XI, 59140U, International Society for Optics and Photonics.
 
Lorenz, C., Carlsen, I.-C., Buzug, T.M., Fassnacht, C., Weese, J. (1997). A multi-scale line filter with automatic scale selection based on the Hessian matrix for medical image segmentation. In: International Conference on Scale-Space Theories in Computer Vision. Springer, pp. 152–163.
 
Malladi, R., Sethian, J.A., Vemuri, B.C. (1995). Shape modeling with front propagation: a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2), 158–175.
 
Mallat, S. (1999). A Wavelet Tour of Signal Processing. Elsevier.
 
Mustafa, W.A.B.W., Yazid, H., Yaacob, S.B., Basah, S.N.B. (2014). Blood vessel extraction using morphological operation for diabetic retinopathy. In: 2014 IEEE Region 10 Symposium. IEEE, pp. 208–212.
 
Navid, M., Hamidpour, S.S.F., Khajeh-Khalili, F., Alidoosti, M. (2020). A novel method to infrared thermal images vessel extraction based on fractal dimension. Infrared Physics & Technology, 107, 103297.
 
Prinet, V., Monga, O., Ge, C., Xie, S., Ma, S. (1996). Thin network extraction in 3D images: application to medical angiograms. In: Proceedings of 13th International Conference on Pattern Recognition, Vol. 3. IEEE, pp. 386–390.
 
Ron, A., Shen, Z. (1997). Affine systems inL2 (Rd): the analysis of the analysis operator. Journal of Functional Analysis, 148(2), 408–447.
 
Rothwell, P.M., Eliasziw, M., Gutnikov, S., Fox, A.J., Taylor, D.W., Mayberg, M., Warlow, C.P., Barnett, H., Collaboration, C.E.T., et al.(2003). Analysis of pooled data from the randomised controlled trials of endarterectomy for symptomatic carotid stenosis. The Lancet, 361(9352), 107–116.
 
Saha, P.K., Udupa, J.K., Odhner, D. (2000). Scale-based fuzzy connected image segmentation: theory, algorithms, and validation. Computer Vision and Image Understanding, 77(2), 145–174.
 
Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R. (1998). Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis, 2(2), 143–168.
 
Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.C. (2005). The dual-tree complex wavelet transform. IEEE Signal Processing Magazine, 22(6), 123–151.
 
Suri, J.S., Laxminarayan, S. (2003). Angiography and Plaque Imaging: Advanced Segmentation Techniques. CRC Press.
 
Udupa, J.K., Samarasekera, S. (1996). Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graphical Models and Image Processing, 58(3), 246–261.
 
Udupa, J.K., Odhner, D., Tian, J., Holland, G., Axel, L. (1997). Automatic clutter-free volume rendering for MR angiography using fuzzy connectedness. In: Medical Imaging 1997: Image Processing, Vol. 3034. International Society for Optics and Photonics. pp. 114–119.
 
Unser, M. (1995). Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing, 4(11), 1549–1560.
 
Wells, W.M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A. (1996). Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, 15(4), 429–442.
 
Wilson, D.L., Noble, J.A. (1997). Segmentation of cerebral vessels and aneurysms from MR angiography data. In: Biennial International Conference on Information Processing in Medical Imaging. Springer, pp. 423–428.
 
Wilson, D.L., Noble, J.A. (1999). An adaptive segmentation algorithm for time-of-flight MRA data. IEEE Transactions on Medical Imaging, 18(10), 938–945.

Biographies

Abdollahi Farid
f.abdollahi@tabrizu.ac.ir

F. Abdollahi was born in 1988. He is a PhD student of Applied Mathematics at Faculty of Mathematical Sciences, University of Tabriz, Iran. His research interests include image processing and wavelets.

Lakestani Mehrdad
lakestani@tabrizu.ac.ir

M. Lakestani received his BSc in applied mathematics from Department of Mathematics at the University of Tabriz, in 1998, MSc and PhD in applied mathematics in 2000 and 2005, respectively, at Amirkabir University of Technology. He has been a professor of mathematics at the University of Tabriz since 2015. His research interests include wavelets, numerical methods, and image processing.

Razzaghi Mohsen
razzaghi@math.msstate.edu

M. Razzaghi received his BSc degree in mathematics and a PhD degree in applied mathematics both from the University of Sussex in England. Since 1986, he has been with the Department of Mathematics and Statistics at Mississippi State University, where he is currently a professor and the department head. He was the recipient of two Fulbright scholar programmes, one in 2011–2012 and another in 2015–2016, and one Fulbright specialist programme in 2019, in Romania. His current area of research centres on orthogonal functions, optimal control, wavelets, fractional calculus, and their applications in mathematical modelling, and engineering. He has over 190 refereed journal publications in mathematics, mathematical physics, and engineering. One of his papers, coauthored with one of his PhD students, was cited over 700 times.


Full article Cited by PDF XML
Full article Cited by PDF XML

Copyright
© 2021 Vilnius University
by logo by logo
Open access article under the CC BY license.

Keywords
VESSEL extraction MRA images TFA algorithm EMS algorithm

Metrics
since January 2020
1305

Article info
views

683

Full article
views

820

PDF
downloads

186

XML
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

INFORMATICA

  • Online ISSN: 1822-8844
  • Print ISSN: 0868-4952
  • Copyright © 2023 Vilnius University

About

  • About journal

For contributors

  • OA Policy
  • Submit your article
  • Instructions for Referees
    •  

    •  

Contact us

  • Institute of Data Science and Digital Technologies
  • Vilnius University

    Akademijos St. 4

    08412 Vilnius, Lithuania

    Phone: (+370 5) 2109 338

    E-mail: informatica@mii.vu.lt

    https://informatica.vu.lt/journal/INFORMATICA
Powered by PubliMill  •  Privacy policy