Summary
Capillaroscopy is one of the best medical diagnostic tools for early detection of scleroderma spectrum disorders. The diagnostic process is based on capillary (small blood vessel) study using a microscope. Key step in capillaroscopy diagnosis is extraction of capillaries. The paper presents a novel semi-automatic method of capillary vessel tracking, which is a non-directional graph creation method. Selection of neighboring vertexes location is its key component. It is performed by model identification. Four capillary model classes are proposed, all using data represented in polar coordinates.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mahler, F., Saner, H., Bose, C.: Local cold exposure test for capillaroscopic examination of patients with Raynaud’s syndrome. Microvascular Research 33, 422–427 (1987)
Maricq, M.R.: Raynaud’s phenomen and microvascular abnormalities in scleroderma (systemic sclerisis). Systemic sclerosis, 151–166 (1998)
Houtman, P.M., Kallenberg, C.G.M., Fidler, V., et al.: Diagnostic significance of nailfold capillary patterns in patients with Raynaud’s phenomen. Journal of Rheumatology 13, 556–563 (1986)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating Blood Vessels in Retinal Images by Piece-wise Threshold Probing of a Matched Filter Response. IEEE Transactions on Medical Imaging (2000)
Jiang, Y., Bainbridge-Smith, A., Morris, A.B.: Blood Vessel Tracking in Retinal Images. In: Proc. of IVC New Zealand 2007, pp. 126–131 (2007)
Boldak, C., Rolland, Y., Toumoulin, C.: An Improved Model-Based Vessel Tracking Algorithm with Application to Computed Tomography Angiography. Biocybernetics And Biomedical Engineering 23(1), 41–63 (2003)
Sainthillier, J.-M., Gharbi, T., Muret, P., Humbert, P.: Skin capillary network recognition and analysis by means of neural algorithms. Skin Research and Technology 11, 9–16 (2005)
Condurache, A.P., Aach, T.: Vessel Segmentation in Angiograms using Hysteresis Thresholding. In: Proc. of 9th IAPR, pp. 269–272 (2005)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering (1998)
Kwasnicka, H., Paradowski, M., Borysewicz, K.: Capillaroscopy Image Analysis as an Automatic Image Annotation Problem. In: Proc. of CISIM 2007, pp. 266–271 (2007)
Farzin, H., Abrishami-Moghaddam, H., Moin, M.-S.: A Novel Retinal Identification System. EURASIP Journal on Advances in Signal Processing (2008)
Cornea, N.D.: Curve-Skeletons: Properties, Computation and Applications. PhD Thesis, The State University of New Jersey (2007)
Kirbas, C., Quek, F.: A Review of Vessel Extraction Techniques and Algorithms. ACM Comput. Surv. 36(2), 81–121 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Paradowski, M., Kwasnicka, H., Borysewicz, K. (2009). Capillary Blood Vessel Tracking Using Polar Coordinates Based Model Identification. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-93905-4_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-93904-7
Online ISBN: 978-3-540-93905-4
eBook Packages: EngineeringEngineering (R0)