PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Thresholding techniques for segmentation of atherosclerotic plaque and lumen areas in vascular arteries

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper develops the automatic methods of segmentation of the blood vessel area in the images of the multi-slice computed tomography, allowing to separate the lumen from the atherosclerotic plaque areas. The solution is based on the application of different implementations of thresholding, including between class variance in a bimodal mode, Gaussian mixture modeling, clustering technique, polynomial and multilayer perceptron approximations. These methods are compared with many examples of arteries of different percentage of the plaque occupancy in the iliac and femoral arteries. The numerical results of segmentation have been verified by the medical experts and prove its usefulness in medical practice. The presented system can find application in an automatic evaluation of the atherosclerosis progression/regression of patients on the basis of sequence of Computed Tomography slice images.
Rocznik
Strony
269--280
Opis fizyczny
Bibliogr. 15, wykr., rys.
Twórcy
  • Department of Pathology, Military Institute of Medicine, 128 Szaserow St., 04-141 Warsaw, Poland
  • Department of Electrical Eng. Theory, Information and Measurement Systems, Warsaw University of Technology, 1 Politechniki Sq., 00-661 Warsaw, Poland
  • markiewt@iem.pw.edu.pl
  • Department of Vascular Surgery, Military Institute of Medicine, 128 Szaserow St., 04-141 Warsaw, Poland
autor
  • Department of Electrical Eng. Theory, Information and Measurement Systems, Warsaw University of Technology, 1 Politechniki Sq., 00-661 Warsaw, Poland
  • Military University of Technology, 2 Kaliskiego St., 00-908 Warsaw, Poland
  • Department of Vascular Surgery, Military Institute of Medicine, 128 Szaserow St., 04-141 Warsaw, Poland
  • Department of Pathology, Military Institute of Medicine, 128 Szaserow St., 04-141 Warsaw, Poland
  • Department of Radiology, Military Institute of Medicine, 128 Szaserow St., 04-141 Warsaw, Poland
Bibliografia
  • [1] E.G. Bovenkamp, J. Dijkstra, J.G. Bosch, and J.H. Reiber, “Multi-agent segmentation of IVUS images”, Pattern Recogn. 37 (4), 647-663 (2004).
  • [2] N. Passat, C. Ronse, and J. Baruthio, “Region-growing segmentation of brain vessels: an atlas-based automatic approach”, J. Magn. Reson. Imaging. 21 (6), 715-725 (2005).
  • [3] N. Passat, C. Ronse, and J. Baruthio, “Watershed and multimodal data for vessel segmentation: application to the superior sagittal sinus”, Image Vision Comput. 25 (4), 512-521 (2007).
  • [4] P. Soille, Morphological Image Analysis, Principles and Applications, Springer, Berlin, 2003.
  • [5] Y. Shang, R. Deklerck, E. Nyssen, A. Markova, J. de Mey, X. Yang, and K. Sun, “Vascular active contour for vessel tree segmentation”, IEEE Trans. Biomed. Eng. 58 (4), 1023-1032 (2011).
  • [6] T. Markiewicz, P. Wisniewski, and S. Osowski, “Comparative analysis of the methods for accurate recognition of cells in the nuclei staining of the Ki-67 in neuroblastoma and ER/PR status staining in breast cancer”, Anal. Quant. Cytol. Histol. 31 (1), 49-62 (2009).
  • [7] B. Raman, R. Raman, G.D. Rubin, and S. Napel, “Automated tracing of the adventitial contour of aortoiliac and peripheral arterial walls in CT angiography (CTA) to allow calculation of non-calcified burden”, J. Digit Imaging 24 (6), 1078-1086 (2011).
  • [8] T. Schepis, M. Marwan, T. Pflederer, M. Seltmann, D. Ropers, W.G. Daniel, and S. Achenbach, “Quantification of noncalcified coronary atherosclerotic plaques with dual-source computed tomography: comparison with intravascular ultrasound”, Heart 96 (8), 610-615 (2010).
  • [9] G. Korosoglou, D. Mueller, S. Lehrke, H. Steen, W. Hosch, T. Heye, H.U. Kauczor, E. Giannitis, and H.A. Katus, “Quantitative assessment of stenosis severity and atherosclerotic plaque composition using 256-slice computed tomography”, Eur. Radiol. 20, 1841-1850 (2010).
  • [10] J. Kittler, J. Illingworth, “Minimum error thresholding”, Pattern Recogn. 19 (1), 41-47 (1986).
  • [11] N. Otsu, “A threshold selection method from grey-level histograms”, IEEE Trans. Sys. Man. Cyb. 9 (1), 62-66 (1979).
  • [12] S.S. Reddi, F.S. Rudin, and H.R. Keshavan, “An optimal multiple threshold scheme for image segmentation”, IEEE Trans. Sys. Man. Cyb. 14 (4), 661-665 (1984).
  • [13] T. Kurita, N. Otsu, and N. Adbelmalek, “Maximum likelihood thresholding based on population mixture mode”, Pattern Recogn. 25 (10), 1231-1240 (1992).
  • [14] O. Demirkaya, M.H. Asyali, and P.H. Sahoo, Image Processing with MATLAB: Applications in Medicine and Biology, CRC Press, Boca Raton, 2009.
  • [15] S. Osowski, Methods and Tools of Data Mining, BTC, Warszawa, 2013, (in Polish).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-15379913-abd4-4c0e-a012-d7bacdb77e50
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.