Abstract
Dictionaries are known tools used in different branches of image processing like edge detection, inpainting and, etc. Segmentation is the task of extracting an object as the part of a particular image. The common drawback of different segmentation methods is that they perform the extraction task incompletely. Tasks like edge detection, denoising and smoothing, as the parts of segmentation, can be done through applying the dictionaries. In this paper, we propose three new contrast stretching function. Based on one of the stretching functions and shearlets as a dictionary, we improved the previous version of a method that has been used in binary segmentation for magnetic resonance angiography images (MRI). We also introduce a three-stage binary image segmentation algorithm for vessel segmentation in MRI images. There are some disadvantages in recent proposed methods when dealing with extracting vessels of medical images. Our algorithm does the task with a more accurate extraction in detecting vessels having low intensity and weak edges in MRI.















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Notes
Tight-frame-based algorithm.
TFA with Eigenvector.
Shearlet-based algorithm.
References
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277
Chapman BE, Stapelton JO, Parker DL (2004) Intracranial vessel segmentation from time-of-flight mra using pre-processing of the mip z-buffer: accuracy of the zbs algorithm. Med Image Anal 8(2):113–126
Chen J, Amini AA (2004) Quantifying 3-d vascular structures in mra images using hybrid pde and geometric deformable models. IEEE Trans Med Imaging 23(10):1251–1262
Dong B, Chien A, Shen Z (2010) Frame based segmentation for medical images. Commun Math Sci 32(4):1724–1739
Franchini E, Morigi S, Sgallari F (2008) Segmentation of 3d tubular structures by a pde-based anisotropic diffusion model. In: MMCS. Springer, pp 224–241
Gooya A, Liao H, Matsumiya K, Masamune K, Masutani Y, Dohi T (2008) A variational method for geometric regularization of vascular segmentation in medical images. IEEE Trans Image Process 17(8):1295–1312
Krissian K, Malandain G, Ayache N, Vaillant R, Trousset Y (2000) Model-based detection of tubular structures in 3d images. Comput Vis Image Underst 80(2):130–171
Lorigo LM, Faugeras OD, Grimson WEL, Keriven R, Kikinis R, Nabavi A, Westin C-F (2001) Curves: curve evolution for vessel segmentation. Med Image Anal 5(3):195–206
Sum K, Cheung PY (2008) Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans Biomed Eng 55(1):358–360
Yan P, Kassim AA (2006) Segmentation of volumetric mra images by using capillary active contour. Med Image Anal 10(3):317–329
Zonoobi D, Kassim AA, Shen W (2009) Vasculature segmentation in mra images using gradient compensated geodesic active contours. J Signal Process Syst 54(1–3):171–181
Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int J Comput Vis 72(2):195–215
Kirbas C, Quek F (2004) A review of vessel extraction techniques and algorithms. ACM Comput Surv (CSUR) 36(2):81–121
Moccia S, De Momi E, El Hadji S, Mattos LS (2018) Blood vessel segmentation algorithms-review of methods, datasets and evaluation metrics. Comput Methods Progr Biomed 158:71–91
McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):91–108
Hassan H, Farag AA (2003) Cerebrovascular segmentation for mra data using level sets. In: International congress series, vol 1256. Elsevier, pp 246–252
Scherl H, Hornegger J, Prümmer M, Lell M (2007) Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3d cta data sets. Med Image Anal 11(1):21–34
Cai X, Chan R, Morigi S, Sgallari F (2013) Vessel segmentation in medical imaging using a tight-frame-based algorithm. SIAM J Imaging Sci 6(1):464–486
Melinščak M, Prentašić P, Lončarić S (2015) Retinal vessel segmentation using deep neural networks. In: 10th international conference on computer vision theory and applications (VISAPP 2015)
Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T (2015) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(1):109–118
Fu H, Xu Y, Lin S, Wong DWK, Liu J (2016) Deepvessel: retinal vessel segmentation via deep learning and conditional random field. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 132–139
Fabijańska A (2018) Segmentation of corneal endothelium images using a u-net-based convolutional neural network. Artif Intell Med 88:1–13
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241
Arivazhagan S, Ganesan L (2003) Texture segmentation using wavelet transform. Pattern Recognit Lett 24(16):3197–3203
Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4(11):1549–1560
Häuser S, Steidl G (2013) Convex multiclass segmentation with shearlet regularization. Int J Comput Math 90(1):62–81
Cigaroudy LS, Aghazadeh N (2015) A binary-segmentation algorithm based on shearlet transform and eigenvectors. In: 2015 2nd international conference on pattern recognition and image analysis (IPRIA). IEEE, pp 1–8
Binh NT (2017) Increasing the segmentation of retinal blood vessels in shearlet domain. In: International conference on the development of biomedical engineering in Vietnam. Springer, pp 357–361
Guo Y, Budak Ü, Şengür A, Smarandache F (2017) A retinal vessel detection approach based on shearlet transform and indeterminacy filtering on fundus images. Symmetry 9(10):235
Cai X, Chan RH, Morigi S, Sgallari F (2011) Framelet-based algorithm for segmentation of tubular structures. In: International conference on scale space and variational methods in computer vision. Springer, pp 411–422
Aghazadeh N, Cigaroudy LS (2014) A multistep segmentation algorithm for vessel extraction in medical imaging. arXiv:1412.8656
Kutyniok G et al (2012) Shearlets: multiscale analysis for multivariate data. Springer, Berlin
Kanghui G, Gitta K, Demetrio L (2006) Sparse multidimensional representations using anisotropic dilation and shear operators, Wavelets and Splines (Athens, GA, 2005). Nashboro Press, Nashville, TN, pp 189–201
Dan Z, Chen X, Gan H, Gao C (2011) Locally adaptive shearlet denoising based on Bayesian map estimate. In: 2011 sixth international conference on image and graphics (ICIG). IEEE, pp 28–32
Donoho D, Kutyniok G (2009) Geometric separation using a wavelet–shearlet dictionary. In: SAMPTA’09, pp Special-session
Easley GR, Labate D, Colonna F (2009) Shearlet-based total variation diffusion for denoising. IEEE Trans Image Process 18(2):260–268
Yi S, Labate D, Easley GR, Krim H (2009) A shearlet approach to edge analysis and detection. IEEE Trans Image Process 18(5):929–941
Reisenhofer R, Kiefer J, King EJ (2016) Shearlet-based detection of flame fronts. Exp Fluids 57(3):41
Häuser S, Steidl G (2012) Fast finite shearlet transform. arXiv:1202.1773
Kutyniok G, Shahram M, Zhuang X (2012) Shearlab: a rational design of a digital parabolic scaling algorithm. SIAM J Imaging Sci 5(4):1291–1332
Shearlab. www.shearlab.org. Accessed 30 Jan 2017
Gonzalez RC (2007) Woods RE, image processing, digital image processing, vol 2
Dong B, Shen Z, et al (2010) MRA based wavelet frames and applications. IAS lecture notes series, summer program on the mathematics of image processing, Park City Mathematics Institute, vol 19
Zhao H-K, Chan T, Merriman B, Osher S (1996) A variational level set approach to multiphase motion. J Comput Phys 127(1):179–195
Buenestado P, Acho L (2018) Image segmentation based on statistical confidence intervals. Entropy 20(1):46
Hansen PC, Nagy JG, Oleary DP (2006) Deblurring images: matrices, spectra, and filtering. SIAM, Philadelphia
Dataset of Medical Images. http://www.osirix-viewer.com. Accessed 30 Jan 2017
Lessig C, Petersen P, Schäfer M (2019) Bendlets: a second-order shearlet transform with bent elements. Appl Comput Harmon Anal 46(2):384–399
Acknowledgements
We would like to thank Professor M. Poureisa (Tabriz University of Medical Sciences) and Azar Mehr MRI Center for providing us some sample images. The Nasser Aghazadeh would like to thanks Professor Gitta Kutyniok for her support during Nasser Aghazadeh's visit in institut für mathematik, Technische Universität Berlin.
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Mirzafam, M., Aghazadeh, N. A three-stage shearlet-based algorithm for vessel segmentation in medical imaging. Pattern Anal Applic 24, 591–610 (2021). https://doi.org/10.1007/s10044-020-00915-3
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DOI: https://doi.org/10.1007/s10044-020-00915-3