Medical ultrasound image segmentation using genetic active contour
ABSTRACT
Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation; however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images.
Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation; however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images.
Cite this paper
nullTalebi, M. , Ayatollahi, A. and Kermani, A. (2011) Medical ultrasound image segmentation using genetic active contour. Journal of Biomedical Science and Engineering, 4, 105-109. doi: 10.4236/jbise.2011.42015.
nullTalebi, M. , Ayatollahi, A. and Kermani, A. (2011) Medical ultrasound image segmentation using genetic active contour. Journal of Biomedical Science and Engineering, 4, 105-109. doi: 10.4236/jbise.2011.42015.
References
[1] Zhu, C.M., Gu, G.C., Liu, H.B., Shen, J. And Yu, H.L., (2008) Segmentation of ultrasound image based on cluster ensemble. IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, Wuhan, 21-22 December 2008, 418-421. doi:10.1109/KAMW.2008.4810513 [2] Deka, B. and Ghosh, D. (2006) Watershed segmentation for medical ultrasound images. IEEE International Conference on Systems, Man, and Cybernetics, 6, 3186-3191. doi:10.1109/ICSMC.2006.384607 [3] Li, L., Fu, Y.X., Bai, P.R. and Mao, W.J. (2009) Medical ultrasound image segmentation based on improved watershed scheme. The 3rd International Conference on Bioinformatics and Biomedical Engineering, Beijing, 11-13 June 2009, 1-4. [4] Li, L.H., Lin, J.L., Li, D.Y. and Wang, T.F. (2007) Segmentation of Medical Ultrasound Image Based on Markov Random Field. The 1st International Conference on Bioinformatics and Biomedical Engineering, Wuhan, 6-8 July 2007, 968-971. [5] Sarti, A., Corsi, C., Mazzini, E. and Lamberti, C. (2005) Maximum likelihood segmentation of ultrasound images with rayleigh distribution. IEEE Transactions on Ultrasonic’s, Ferroelectrics and Frequency Control, 52, 947-960. doi:10.1109/TUFFC.2005.1504017 [6] Michailovich, O. and Tannenbaum, A. (2007) Segmentation of medical ultrasound images using active contours. IEEE International Conference on Image Processing, 6, 513-516. doi:10.1109/ICIP.2007.4379878 [7] Kass, M., Witkin, A. and Terzopoulos, D. (1988) Snakes: active contour models. International Journal of Computer Vision, 1, 321-331. doi:10.1007/BF00133570 [8] Cohen, L.D. (1991) On active contour models and balloons. CVGIP: Image Understanding, 53, 211-218. [9] Xu, C.Y. and Prince, J.L. (1998) Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7, 359-369. [10] Cohen, L.D. and Cohen, I. (1993) Finite element methods for active contour models and balloons for 2-D and 3-D images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 1131-1147. doi:10.1109/34.244675 [11] L. Ballerini, (1993) Genetic snakes for medical images segmentation. In: Poli, R. Ed., Evolutionary image Analysis, Signal Processing and Telecommunications, Springer, London, 59-73. [12] GholamAli Rezaei Rad, Mehdi Kashanian, (2006) Extraction of the breast cancer tumor in mammograms using genetic active contour. International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 11-14 December 2006, 30-33. [13] Sivanandam, S.N. and Deepa, S.N. (2008) Introduction to genetic algorithms. Springer-Verlag, New York. [14] Gonzalez, R.C. and Woods, R.E. (2002) Digital image processing. 2nd Edition, Pearson Education, Inc., Upper Saddle River, New Jersey.
[1] Zhu, C.M., Gu, G.C., Liu, H.B., Shen, J. And Yu, H.L., (2008) Segmentation of ultrasound image based on cluster ensemble. IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, Wuhan, 21-22 December 2008, 418-421. doi:10.1109/KAMW.2008.4810513 [2] Deka, B. and Ghosh, D. (2006) Watershed segmentation for medical ultrasound images. IEEE International Conference on Systems, Man, and Cybernetics, 6, 3186-3191. doi:10.1109/ICSMC.2006.384607 [3] Li, L., Fu, Y.X., Bai, P.R. and Mao, W.J. (2009) Medical ultrasound image segmentation based on improved watershed scheme. The 3rd International Conference on Bioinformatics and Biomedical Engineering, Beijing, 11-13 June 2009, 1-4. [4] Li, L.H., Lin, J.L., Li, D.Y. and Wang, T.F. (2007) Segmentation of Medical Ultrasound Image Based on Markov Random Field. The 1st International Conference on Bioinformatics and Biomedical Engineering, Wuhan, 6-8 July 2007, 968-971. [5] Sarti, A., Corsi, C., Mazzini, E. and Lamberti, C. (2005) Maximum likelihood segmentation of ultrasound images with rayleigh distribution. IEEE Transactions on Ultrasonic’s, Ferroelectrics and Frequency Control, 52, 947-960. doi:10.1109/TUFFC.2005.1504017 [6] Michailovich, O. and Tannenbaum, A. (2007) Segmentation of medical ultrasound images using active contours. IEEE International Conference on Image Processing, 6, 513-516. doi:10.1109/ICIP.2007.4379878 [7] Kass, M., Witkin, A. and Terzopoulos, D. (1988) Snakes: active contour models. International Journal of Computer Vision, 1, 321-331. doi:10.1007/BF00133570 [8] Cohen, L.D. (1991) On active contour models and balloons. CVGIP: Image Understanding, 53, 211-218. [9] Xu, C.Y. and Prince, J.L. (1998) Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7, 359-369. [10] Cohen, L.D. and Cohen, I. (1993) Finite element methods for active contour models and balloons for 2-D and 3-D images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 1131-1147. doi:10.1109/34.244675 [11] L. Ballerini, (1993) Genetic snakes for medical images segmentation. In: Poli, R. Ed., Evolutionary image Analysis, Signal Processing and Telecommunications, Springer, London, 59-73. [12] GholamAli Rezaei Rad, Mehdi Kashanian, (2006) Extraction of the breast cancer tumor in mammograms using genetic active contour. International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 11-14 December 2006, 30-33. [13] Sivanandam, S.N. and Deepa, S.N. (2008) Introduction to genetic algorithms. Springer-Verlag, New York. [14] Gonzalez, R.C. and Woods, R.E. (2002) Digital image processing. 2nd Edition, Pearson Education, Inc., Upper Saddle River, New Jersey.