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Evaluation of Edge Configuration in Medical Echo Images Using Genetic Algorithms

Authors: Ching-Fen Jiang


Edge detection is usually the first step in medical image processing. However, the difficulty increases when a conventional kernel-based edge detector is applied to ultrasonic images with a textural pattern and speckle noise. We designed an adaptive diffusion filter to remove speckle noise while preserving the initial edges detected by using a Sobel edge detector. We also propose a genetic algorithm for edge selection to form complete boundaries of the detected entities. We designed two fitness functions to evaluate whether a criterion with a complex edge configuration can render a better result than a simple criterion such as the strength of gradient. The edges obtained by using a complex fitness function are thicker and more fragmented than those obtained by using a simple fitness function, suggesting that a complex edge selecting scheme is not necessary for good edge detection in medical ultrasonic images; instead, a proper noise-smoothing filter is the key.

Keywords: edge detection, ultrasonic images, speckle noise

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[1] Y. Q. Zhan and D. G. Shen, "Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method," IEEE Trans. on Medical Imaging, vol. 25, pp. 256-272, Mar 2006.
[2] W. D. Richard and C. G. Keen, "Automated texture-based segmentation of ultrasound images of the prostate," Comput Med Imaging Graph, vol. 20, pp. 131-40, May-Jun 1996.
[3] C. M. Wu, and Y. C. Chen, "Texture feature for classification of ultrasonic liver images", IEEE Trans. Med. Imaging, vol. 11, pp. 141-152, 1992.
[4] K. N. Bhanu Prakash, et al., "Fetal lung maturity analysis using ultrasound image features," IEEE Trans.on Information Technology in Biomedicine, vol. 6, pp. 38-45, 2002.
[5] S. Pavlopoulos, and et. al. "Fuzzy neural Network-Based Texture Analysis of Ultrasonic Images", IEEE Engineering in Medicine and Biology, vol.19, no. 1, pp39-47, 2000
[6] C.F. Jiang, "3D image reconstruction of ovarian tumor in the ultrasonic images, Biomedical Engineering-Applications, Basis & Communications", vol. 13; pp 41-46, 2001.
[7] Y. C. Hsu, C.F. Jiang, and C. M. Uang, "Auto-segmentation of ultrasonic images by the genetic algorithm", Journal of Medical and Biological Engineering, vol. 21, no.2 ,pp. 121-126, 2001.
[8] M. Gudmundsson, E.A. El-Kwae, and M.R. Kabuka, "Edge detection in medical images using a genetic algorithm," IEEE Trans. Medical Imaging, 17, pp. 469-474, 1998
[9] P. Perona, and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. Patt. Anal. Mech. Intell., PAMI-12, no. 7, pp. 629-639, 1990
[10] S.R. Wang, Y.N. Sun, F.M. Chang, and C. F. Jiang, "3D image display of fetal ultrasonic image by thin shell," SPIE on Medical Imaging, pp.1478-1488, San Diego, USA, 1999.
[11] R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley, 2002, Ch. 10.