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Enhancing Breast Ultrasound Images using Hough Transform

N. Alamelumangai, Dr. J. Devishree


Problem Statement: Highly widespread and foremost reason for cancer death among women is Breast cancer. It has turn out to be most important health concern in the world over the past 50 years, and its occurrence has mounted in recent years. Early detection is an efficient method to diagnose and supervise breast cancer. Computer-aided detection or diagnosis (CAD) systems can act a major function in the early detection of breast cancer and can decrease the death rate among women with breast cancer. Approach: The purpose of this paper is to provide a better CAD system which detects the cancer in early stages. The proposed system involves three phases such as speckle noise reduction, image enhancement and segmentation. For removing the speckle noise, this paper uses Memetic algorithm. Image enhancement is performed using Hough transform. Finally, the enhanced image is segmented using clustering technique called Modified Fuzzy Possibilistic C-Means technique with Repulsion factor to identify the cancer affected region Results: The proposed enhancement technique for breast ultrasound image is evaluated using the real time ultrasound images. The comparison is performed by means of Mean Square Error between the existing and proposed technique. Mean Square Error for the proposed approach is lesser when compared to the existing approach. Conclusion: The experimental result suggests that the proposed system results in better enhancement in ultrasound image when compared to the conventional technique.


Ultrasound Image, Memetic Algorithm, Hough Transform, Modified Fuzzy Possibilistic C-Means, Repulsion.

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Oelze, M.L.; O'Brien, W.D.; Zachary, J.F, “11B-4 Quantitative Ultrasound Assessment of Breast Cancer Using a Multiparameter Approach,” IEEE Ultrasonics Symposium, Pp. 981 – 984, 2007.

Gefen, S.; Tretiak, O.J.; Piccoli, C.W.; Donohue, K.D.; Petropulu, A.P.; Shankar, P.M.; Dumane, V.A.; Lexun Huang; Kutay, M.A.; Genis, V.; Forsberg, F.; Reid, J.M.; Goldberg, B.B., “ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis,” IEEE Transactions on Medical Imaging, Vol. 22, No. 2, Pp. 170 – 177, 2003.

Winder, A.A.; Jadidian, B.; Muratore, R., “Synthetic Structural Imaging (SSI): A new ultrasound method for tracking breast cancer morphology,” 39th Annual Ultrasonic Industry Association Symposium (UIA), Pp. 1 – 4, 2010.

American Cancer Society, Breast Cancer Facts & Figures 2007-2008, American Cancer Society, 2008.

Cheng, H.D., Cai, X., Chen, X., Hu, L., and Lou, X. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36, 12 (2003), 2967-2991. Mammograms. 2009. June 2009.

Drukker, K., Giger, M.L., Vyborny, C.J., and Mendelson, E. B. Computerized detection and classification of cancer on breast ultrasound. Academic Radiology 11, 5 (2004), 526-535.

Li, X. and Liu, D.C. Ultrasound image enhancement using dynamic filtering. In 4th International Conference on Image and Graphics, 2007, 106-109.

Deshmukh, K.S. and Shinde, G.N. An adaptive color image segmentation. Electronic Letters on Computer Vision and Image Analysis 5, 4 (2005), 12-23, 2005.

Noble, J.A. and D. Boukerroui, D. Ultrasound image segmentation: A survey. IEEE Transactions on Medical Imaging 25, 8 (2006), 987-1010.

Adam, D., Beilin-Nissan, S., Friedman, Z., and Behar, V. The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images. Ultrasonics 44, 2 (2006), 166.

Juan Wachs, Oren Shapira and Helman Stern, "A Method to Enhance the Possibilistic C-Means with Repulsion Algorithm based on Cluster Validity Index", Advances in Intelligent and Soft Computing, Springerlink, Vol. 34, Pp. 77-87, 2006

Zhang, L.C., Wong, E.M.C., Zhang, F., and Zhou, J. Adaptive pyramid filtering for medical ultrasound image enhancement. In 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006, 916-919.

Shi, X., Cheng, H.D., Hu, L., Ju, W., and Tian, J. Detection and classification of masses in breast ultrasound images. Digital Signal Processing 20, 3 (2010), 824- 836.

Madabhushi, A. and Metaxas, D.N. Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions, IEEE Transactions on Medical Imaging 22, 2 (2003), 155- 169.

Noble, J.A. and Boukerroui, D. Ultrasound image segmentation: A survey. IEEE Transactions on Medical Imaging 25, 8 (2006), 987.

Chen, C.-Y. and Ye, F. Particle swarm optimization algorithm and its application to clustering analysis. In IEEE International Conference on Networking, Sensing and Control, 2004, 789-794.

J.-S.R. Jang, ANFIS: adaptive network-based fuzzy inference system, IEEE Trans. Systems, Man, Cybern. 23 (03) (1993) pp.665–685.

S. Haykin, Neural Networks, Prentice-Hall, Englewood Cliffs, NJ, (1998).

Elif Derya Ubeyli, Inan Guler, Teaching Automated Diagnostic systems for Doppler ultrasound blood flow signals to biomedical engineering students using Matlab, International Journal of Engineering Education, Vol 21 (4) (2005),pp.649-667.

I. Kalaykov, G. Tolt, Real-time image noise cancellation based on fuzzy similarity, in: M. Nachtegael et al. (Eds.), Fuzzy Filters for Image Processing, Springer, Berlin, Heidelberg, NewYork, (2003), pp. 54–71.

B. Kosko, Neural Networks and Fuzzy System, Prentice-Hall, Englewood Cliffs, NJ, 1992.

Y. S. Ong, M.H. Lim, N. Zhu, K.W. Wong, “Classification of adaptive memetic algorithms: a comparative study”, IEEE Trans. Syst. Man Cybern. Part B 36 (1) (2006) 141–152.

A. Rafiee Kerachi, M.H. Moradi, M.R. Farzaneh, “Speckle noise reduction in sonography images by using online genetic neuro fuzzy filters”, Proceedings of the 4th Seminars on Fuzzy Sets and its Applications- Iran, (2003), pp. 70-77.


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