Research Article Open Access

Automatic Boundary Detection of Wall Motion in Two-dimensional Echocardiography Images

Faten Abed Ali Dawood1, Rahmita Wirza Rahmat1, Mohd Zamrin Dimon1, Lili Nurliyana2 and Suhaini Bin Kadiman1
  • 1 , Afganistan
  • 2 ,
Journal of Computer Science
Volume 7 No. 8, 2011, 1261-1266

DOI: https://doi.org/10.3844/jcssp.2011.1261.1266

Submitted On: 16 February 2011 Published On: 15 July 2011

How to Cite: Dawood, F. A. A., Rahmat, R. W., Dimon, M. Z., Nurliyana, L. & Kadiman, S. B. (2011). Automatic Boundary Detection of Wall Motion in Two-dimensional Echocardiography Images. Journal of Computer Science, 7(8), 1261-1266. https://doi.org/10.3844/jcssp.2011.1261.1266

Abstract

Problem statement: Medical image analysis is a particularly difficult problem because the inherent characteristics of these images, including low contrast, speckle noise, signal dropouts and complex anatomical structures. An accurate analysis of wall motion in Two-dimensional echocardiography images is “important clinical diagnosis parameter for many cardiovascular diseases”. A challenge most researchers faced is how to speed up the clinical decisions and reduce human error of estimating accurately the true wall movements boundaries if can be done automatically will be a useful tool for assessing these diseases qualitatively and quantitatively. Approach: The proposed method involves three stages: First, the pre-processing stage for image contrast enhancement to reduce speckle-noise and to highlight certain features of interest (i.e., myocardial tissue). In the second stage, we applied the segmentation process using thresholding technique by considering a mean value of pixels intensity as a threshold value to distinct image features (e.g., Background and object). After thresholding implementation, the two most common mathematical morphology operators ‘erosion’ and ‘dilation’ are applied to improve the efficiency of the wall boundary detection process. Finally, Robert’s operator is used as edge detector to identify the wall boundaries. Results: For accuracy measurement, the experimental results of the proposed method are compared to that obtained from medical QLab system qualitatively and quantitatively. Conclusion: The results showed that our proposed method is reliable and its performance accuracy percentages are 50% more acceptable and 42% better than QLab system results.

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Keywords

  • Echocardiography image
  • threshold value
  • edge detection
  • mathematical morphology operators
  • Robert’s operator
  • proposed method
  • QLab system
  • semi-automatic algorithm
  • boundary detection process
  • wall motion