Journal of Computer Science

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

Faten Abed Ali Dawood, Rahmita Wirza Rahmat, Mohd Zamrin Dimon, Lili Nurliyana and Suhaini Bin Kadiman

DOI : 10.3844/jcssp.2011.1261.1266

Journal of Computer Science

Volume 7, Issue 8

Pages 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.

Copyright

© 2011 Faten Abed Ali Dawood, Rahmita Wirza Rahmat, Mohd Zamrin Dimon, Lili Nurliyana and Suhaini Bin Kadiman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.