Classification of Hyperkinetic, Hypokinetic and Normal Segments in Ventriculographic Images, using Centerline Method
Keywords:
left ventricular wall, software platform, ventriculographic images, hypokinesis, hyperkinesis
Abstract
Evaluation of regional left ventricular wall motion is of vital importance at the clinical level, since this cavity is the most susceptible to severe damage in diseases such as arterial hypertension, diabetes mellitus and atherosclerosis. This paper reports on the design of a software platform for the estimation of parameters describing the left ventricular wall motion in ventriculographic images. The system focuses on end-diastolic and end-systolic contours. Firstly is carried out a process of manual segmentation by the specialist, then 100 chords generated between two ventricular contours are quantified, which allows the classification of these segments (normal, hyperkinetic and hypokinetic). At last, the testing process is performed using actual data acquired in the Institute Autonomy Hospital Universitary of University of the Andes IAHULA.Downloads
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References
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[5] N. Ingels, G. Daughters, E. Stinson and E. Alderman, “Evaluation of methods for quantitating left ventricular segmental wall motion in man using myocardial markers as a standard”, Circulation, vol. 61, pp. 966-972, 1980.
[6] H. Gelberg, B. Brundage, S. Glantz and W. Parmley, “Quantitative left ventricular wall motion analysis: a comparison of area, chord and radial methods”, Circulation, vol. 59, pp. 991-1000, 1979.
[7] R. Leighton, S. Wilt and R. Lewis, “Detection of hypokinesis by a quantitative analysis of left ventricular cineangiograms”, Circulation, vol. 50, pp. 121-127, 1974.
[8] S. Antoine, “Extraction et caractérisation du mouvement cardiaque in imagerie scanner multibarrette”, Ph.D. dissertation, Université de Rennes, 2006.
[9] A. Bravo, “Simulación y reconstrucción en 4-D del ventrículo izquierdo en imagenología cardiaca”. Tesis doctoral, Universidad Simón Bolívar, Venezuela, 2006.
[10] General Electric Company, “Innova 2000, digital cardiovascular x-ray imaging system”.
[11] J. Sagardi, “El detector digital en un sistema de imagen cardiovascular”, Revista de Física Médica, vol. 3, pp. 35-38, 2002.
[12] F. Sheehan, D. Stewart, H. Dodge, S. Mitren, E. Bolson and B. Brown, “Variability in the measure ment of regional left ventricular wall motion from contrast angiograms, Circulation, vol. 68, pp. 550-559, 1983.
[13] P. Hough, “Method and Means for Recognizing Complex Patterns”. U.S. Patent 3069654, 1962.
[14] R. Duda and P. Hart, “Use of the Hough Transformation to Detect Lines and Curves in Pictures”, Comm. ACM, vol. 15, pp. 11-15, 1972.
[15] J. Zhang, J. Hu, “Image segmentation based on 2d otsu method with histogram analysis”. International Conference on Computer Science and Software Engineering, vol. 6, pp. 105-108, 2008.
[16] J. Canny, “A computational approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679-698. 1986.
[17] F. Sheehan, E. Bolson, H. Dodge, D. Mathey, J. Schofer, H. Woo. “Advantages and applications of the centerline method for characterizing regional ventricular function”. Circul ition, vol. 74, pp. 293-305. [Online]. Available: http://circ.ahajournals. 1986.
Published
2016-04-04
How to Cite
Velandia-Villamizar, H., Medina-Molina, R., & Mendoza, L. (2016). Classification of Hyperkinetic, Hypokinetic and Normal Segments in Ventriculographic Images, using Centerline Method. ITECKNE, 13(1), 57-63. https://doi.org/https://doi.org/10.15332/iteckne.v13i1.1382
Issue
Section
Research and Innovation Articles