JBiSE  Vol.3 No.11 , November 2010
Medical ultrasound image segmentation by modified local histogram range image method
Abstract: Fast and satisfied medical ultrasound segmentation is known to be difficult due to speckle noises and other artificial effects. Since speckle noise is formed from random signals which are emitted by an ultrasound system, we can’t encounter the same way as other image noises. Lack of information in ultrasound images is another problem. Thus, segmentation results may not be accurate enough by means of customary image segmentation methods. Those methods that can specify undesirable effects and segment them by eliminating artificial effects, should be chosen. It seems to be a complicated work with high computational load. The current study presents a different approach to ultrasound image segmentation that relies mainly on local evaluation, named as local histogram range image method which is modified by means of discrete wavelet transform. Thus, a significant decrease in computational load is then achieved. The results show that it is possible for tissues to be segmented correctly.
Cite this paper: nullKermani, A. , Ayatollahi, A. , Mirzaei, A. and Barekatain, M. (2010) Medical ultrasound image segmentation by modified local histogram range image method. Journal of Biomedical Science and Engineering, 3, 1078-1084. doi: 10.4236/jbise.2010.311140.

[1]   Chan, T. and Vese, L. (2001) Active contours without edges. IEEE Transaction on Image Processing, 10, 266- 272.

[2]   Grau, V., Mewes, A.U.J., Alcaniz, M., Kikinis, R. and Warfield, S.K. (2004) Improved watershed transform for medical image segmentation using prior information IEEE Transactions on Medical Imaging, 23(4), 447-458.

[3]   Wu, J. and Chung, A.C.S. (2007) “A segmentation model using compound Markov random fields based on a boun- dary model,” IEEE Transaction on Image Processing, 16, 241-252.

[4]   Makrogiannis, S., Economou, G., Fotopoulos, S. and Bourbakis, N.G. (2005) Segmentation of colour images using multiscale clustering and graph theoretic region synthesis. IEEE Transaction on System Man and Cybernetics, 35(2), 224-238.

[5]   Semmlow, J.L. (2004) Biosignal and biomedical image processing. CRC Press, Boca Raton.

[6]   Aja-Fern′andez, S., Martin-Fern′andez, M. and Alberola-L′opez, C. (2007) Tissue identification in ultrasound images using Rayleigh local parameter estimation. Proceedings of Bioinformatics and Bioengineering, Boston.

[7]   Wang, B. and Liu, D.C. (2008) A novel edge enhancement method for ultrasound imaging. The 2nd International Conference on Bioinformatics and Biomedical Engineering, 3, 645-649.

[8]   kermani, A., Ayatollahi, A. and Talebi, M. (2010) Segmentation medical ultrasound image based on Local histogram range image. The 3rd International Conference on BioMedical Engineering and Informatics, in Press.

[9]   Shankar, P.M. (2000) A general statistical model for ultrasonic backscattering from tissues. Ultrasonics, Ferroelectrics and Frequency Control, 47(3), 727-736.

[10]   Wagner, R.F., Smith, S.W., Sandrik, J.M. and Lopez, H. (1983) Statistics of speckle in ultrasound B-scans. IEEE Transactions on Sonics and Ultrasonics, 30(3), 156-163.

[11]   Ghofrani, S., Jahed-Motlagh, M.R. and Ayatollahi, A. (2001) An adaptive speckle suppression filter based on Nakagami distribution. Proceeding of IEEE International Conference on Trends in Communications, Bratislava, 1, 84-87.

[12]   Gonzalez, R.C. and Woods, R.E. (2002) Digital image processing. 2nd Edition, Publishing House of Electronics Industry, Beijing, 523-527.

[13]   Topiwala, P.N., Ed., (2007) Wavelet image and video compression. The Springer International Series in Engineering and Computer Science, Cambridge.

[14]   Vetterli, M. H. (1992) Wavelets and filter banks: Theory and design. IEEE Transactions on Signal Processing, 40 (9), 2207-2232.

[15]   Zhenzhu, Y., Yong, Y., Zhenxi, C. and Kongyang, Z. (2009) Study of the de-noising method based on wavelet and fractal. Second International Workshop on Computer Science and Engineering, WCSE’09, 1, 15-19.

[16]   Guo, F., Derong, T.L. and Can, Z. (2005) A new compression algorithm for medical images using wavelet transform. IEEE Conferences on Networking, Sensing and Control, Tucson.

[17]   Gupta, P.K. and Kanhirodan, R. (2006) Wavelet transform based error concealment approach for image denoising. 1st IEEE Conference on Industrial Electronics and Applications, Singapore.

[18]   Xiaojuan, L., Guangshu, H. and Shangkai, G. (1999) De- sign and implementation of a novel compression method in a tele-ultrasound system. IEEE Transactions on Information Technology in Biomedicine, 3(3), pp. 205-213.