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Segmentation of Ultrasound Breast Images: Optimization of Algorithm Parameters

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Applications of Evolutionary Computation (EvoApplications 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6624))

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Abstract

Segmentation of lesions in ultrasound imaging is one of the key issues in the development of Computer Aided Diagnosis systems. This paper presents a hybrid solution to the segmentation problem. A linear filter composed of a Gaussian and a Laplacian of Gaussian filter is used to smooth the image, before applying a dynamic threshold to extract a rough segmentation. In parallel, a despeckle filter based on a Cellular Automata (CA) is used to remove noise. Then, an accurate segmentation is obtained applying the GrowCut algorithm, initialized from the rough segmentation, to the CA-filtered image. The algorithm requires tuning of several parameters, which proved difficult to obtain by hand. Thus, a Genetic Algorithm has been used to find the optimal parameter set. The fitness of the algorithm has been derived from the segmentation error obtained comparing the automatic segmentation with a manual one. Results indicate that using the GA-optimized parameters, the average segmentation error decreases from 5.75% obtained by manual tuning to 1.5% with GA-optimized parameters.

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References

  1. Achim, A., Bezerianos, A., Tsakalides, P.: Novel bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imag. 20, 772–783 (2001)

    Article  Google Scholar 

  2. Benson, S., Blue, J., Judd, K., Harman, J.: Ultrasound is now better than mammography for the detection of invasive breast cancer. America Journal Surgery (2004)

    Google Scholar 

  3. Donoho, D.L.: Denoising by soft-thresholding. IEEE Trans. Inf. Theory 41, 613–627 (1995)

    Article  MATH  Google Scholar 

  4. Favilli, L., Bocchi, L.: Automatic system for the analysis and the discrimination of breast nodules in ultrasound imaging. In: World Congress on Medical Physics and Biomedical Engineering, vol. 25/4 (2009)

    Google Scholar 

  5. Guliato, D., Rangayyan, R.M., Carnielli, W.A., Zuffo, J.A., Desautels, J.E.L.: Segmentation of breast tumors in mammograms by fuzzy region growing. J. of El. Im. 12, 369–378 (2003)

    Article  Google Scholar 

  6. Hernandez, G., Herrmann, H.J.: Cellular automata for elementary image enhancement. Graphical Models and Image Processing 58, 82 (1996)

    Article  Google Scholar 

  7. Huang, Y.L., Chen, D.R.: Watershed segmentation for breast tumor in 2-d sonography. Ultrasound in Medicine Biology 30, 625–632 (2004)

    Article  Google Scholar 

  8. Huang, Y.L., Wang, K.L., Chen, D.R.: Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comp. & Appl. 15, 164–169 (2006)

    Article  Google Scholar 

  9. Kauffmann, C., Piché, N.: Seeded nd medical image segmentation by cellular automaton on gpu. Int. J. CARS 5, 251–262 (2010)

    Article  Google Scholar 

  10. Madabhushi, A., Metaxas, D.: Combining low-high-level and empirical domain knowledge for automated segmentation of breast lesions. IEEE Trans. on Medical Imaging 22 (2003)

    Google Scholar 

  11. Noicolae, M.C., Moraru, L., Onose, L.: Comparative approach for speckle reduction in medical ultrasound images. Romanian J. Biophys. 20, 13–21 (2010)

    Google Scholar 

  12. Nori, J., Vanzi, E., Bazzocchi, M., Bufalini, F.N., Distante, V., Branconi, F., Susini, T.: Role of axillari ultrasound examination in the selection of breast cancer patients for sentinel node biopsy. American Journal of Surgery 193, 16–20 (2007)

    Article  Google Scholar 

  13. Papadimitriou, S., Bezerianos, A.: Multiresolution analysis and denoising of computer performance evaluation data with the wavelet transform. J. Syst. Architect. 42, 55–65 (2010)

    Article  Google Scholar 

  14. Pignalberi, G., Cucchiara, R., Cinque, L., Levialdi, S.: Tuning range image segmentation by genetic algorithm. EURASIP Journal on Applied Signal Processing 8, 780–790 (2003)

    Article  MATH  Google Scholar 

  15. Thangavel, K., Manavalan, R., Aroquiaraj, I.L.: Removal of speckle noise from ultrasound medical image based on special filters: comparative study. ICGST-GVIP Journal 9 (2009)

    Google Scholar 

  16. Vezhnevets, V., Konouchine, V.: Grow-cut - interactive multi-label n-d image segmentation. In: Graphicon (2005)

    Google Scholar 

  17. World Health Organization International (ed.): World Cancer Report. IARC Press (2003)

    Google Scholar 

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Bocchi, L., Rogai, F. (2011). Segmentation of Ultrasound Breast Images: Optimization of Algorithm Parameters. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_17

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  • DOI: https://doi.org/10.1007/978-3-642-20525-5_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20524-8

  • Online ISBN: 978-3-642-20525-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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