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Warianty tytułu
Języki publikacji
Abstrakty
Cardiovascular system diseases are the major causes of mortality in the world. The most important and widely used tool for assessing the heart state is echocardiography (also abbreviated as ECHO). ECHO images are used e.g. for location of any damage of heart tissues, in calculation of cardiac tissue displacement at any arbitrary point and to derive useful heart parameters like size and shape, cardiac output, ejection fraction, pumping capacity. In this paper, a robust algorithm for heart shape estimation (segmentation) in ECHO images is proposed. It is based on the recently introduced variant of the level set method called level set without edges. This variant takes advantage of the intensity value of area information instead of module of gradient which is typically used. Such approach guarantees stability and correctness of algorithm working on the border between object and background with small absolute value of image gradient. To reassure meaningful results, the image segmentation is proceeded with automatic Region of Interest (ROI) calculation. The main idea of ROI calculations is to receive a triangle-like part of the acquired ECHO image, using linear Hough transform, thresholding and simple mathematics. Additionally, in order to improve the images quality, an anisotropic diffusion filter, before ROI calculation, was used. The proposed method has been tested on real echocardiographic image sequences. Derived results confirm the effectiveness of the presented method.
Czasopismo
Rocznik
Tom
Strony
305--314
Opis fizyczny
Bibliogr. 16 poz., rys., tab., wykr.
Twórcy
autor
autor
- AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Measurement and Instrumentation, al. Mickiewicza 30, 30-059 Cracow, Poland, skalski@agh.edu.pl
Bibliografia
- [1] http://www.rejestrozw.republika.pl/
- [2] Kass, M., Witkin, A., Terzopoulos, D., (1988). Snakes: Active contour models. International Journal on Computer Vision, 1, 321-331.
- [3] Gonzalez, R.C., Woods, R.E. (2007). Digital Image Processing. Prentice Hall.
- [4] Osher, S., Sethian, J.A. (1988). Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 114, 12 - 49.
- [5] Osher, S., Paragios, N. (2003). Geometric Level Set Methods in Imaging, Vision, and Graphics. Springer-Verlag. New York.
- [6] Ledesma-Carbayo, M.J. et al. (2005). Spatio-Temporal Nonrigid Registration for Ultrasound Cardiac Motion Estimation. IEEE Transactions on Medical Imaging. 24(9), 1113-1126.
- [7] Nascimento, J.C., Sanches, J.S., Marques, J.S. (2006). A Method the Dynamic Analysis of the Heart Using a Lyanounov Based Denoising Algorithm. Proc. IEEE EMBC, 4828-4831.
- [8] Bharali, U., Ghosh, D. (2006). Cardiac Motion Estimation from Echocardiographic Image Sequence using Unsupervised Active Contour Tracker. Proc. ICARCV.
- [9] Zhuang, L., et al. (2007). Volumetric Meshfree Framework for Joint Segmentation and Motion Tracking of the Left Ventricle. Proc. ISBI, 396-399.
- [10] Yang, L., et al. (2008). A Fast and Accurate Tracking Algorithm of Left Ventricles in 3D Echocardiograpchy. Proc. ISBI, 221-224.
- [11] Luo, J., Konofagou, E.E. (2008). High-Frame Rate. Full-View Myocardial Elastography with Automated Contour Tracking in Murine Left Ventricles in Vivo. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 55, 240-248.
- [12] Turcza, P. (10-14 May 2009). USG image denoising as an earky stage In methods detecting common carotid artery infima-media thickness. IX Symposium on Modelling and Measurements in Medicine: Krynica, 171-174. (in Polish).
- [13] Duda, R.O., Hart, P.E. (1972). Use of the Hough Transformation to Detect Lines and Curves in Pictures. Comm. ACM, 15, 11-15.
- [14] Chan, T.F., Vese, L.A. (2001). Active contours without edges, IEEE Transactions on Image Processing, 10(2), 266-277.
- [15] Ryniewicz, A. (2010). Accuracy assessment of shape mapping using Computer Tomography. Metrology and Measurement Systems, XVII(3), 481-492.
- [16] Fenster, A., Chiu, B. (2005). Evaluation of segmentation algorithms for medical imaging. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Confernece, 7196-7189.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0079-0012