Automatic segmentation of multiple lesions in ultrasound breast image

  • Authors

    • Chelladurai R
    • Selvakumar R
    • S Poonguzhali
    2018-03-18
    https://doi.org/10.14419/ijet.v7i2.7.10919
  • BPN (Back Propagated Network), Breast lesion, Seed point, Tan Processing, Ultrasound (US) .

  • Abstract

    Breast cancer is one of the leading cancer that affects woman all around the world. Nowadays ultra sound imaging technique is used to diagnose various cancer because of its non-ionizing, on-invasive, and cheap cost. Breast lesion region in ultrasound images are classified depending upon the contour, shape, size and textural features of the segmented region. Seed point is the initial step in segmentation of lesion regions and if that point is located outside the lesion region, it leads to wrong segmentation which results in misclassification of the lesion regions. To avoid this, most of the time the seed point is located manually. In order to avoid this manual intervention, we are proposing a novel method in locating the seed point and also segmenting the breast lesion region automatically. In this method, the image is processed with tan function for effective distinguishing of breast lesion and normal region. Then using the trained neural network, the seed point is automatically located inside the lesion region and from the seed point the region of the lesion is grown and segmented automatically. Most of the past works on automatic segmentation of lesion had concentrated only in single lesion region, but using this proposed method, we were able to automatically segment multiple lesion regions in the image. Outcome of the proposed method is to detect automatically and dynamically separate the lesion region in the range between 90% to 97.5% of images.

     

  • References

    1. [1]. Telagarapu Prabhakar, S.Poonguzhali ,Automatic Detection and Classification of Benign and Malignant Lesions in Breast Ultrasound Images using Texture Morphological and Fractal Features, 978-1-5386-0882-1/17/2017 IEEE

      [2]. Guita Rahbar, Angela C. Sie, Gail C. Hansen, Jeffrey S. Prince, Michelle L. Melany. Benign versus Malignant Solid Breast Masses: US Differentiation. 1999. Radiology.

      [3]. Hong-Ting Chen, Yi-Ping Lien, Po-Ting Liu, Dar-Ren Chen and Yu-Len Huang. Characterization of Benign and Malignant Solid Breast Masses Using Vascular Morphology in 3D Power Doppler Ultrasound Images. 2010.3rd International Conference on Biomedical Engineering and Informatics.

      [4]. Hsieh-Wei Lee, King-Chu Hung, Bin-Da Li, Sheau-Fang Lei, and Hsin-Wen Ting. Realization of high octave decomposition for breast cancer feature extraction on ultrasound images. 2011. IEEE Transactions on circuits and system. Vol 58, NO. 6.

      [5]. Juan Shan, H. D., Cheng, Yuxuan Wang. A novel automatic seed point selection algorithm for breast ultrasound images. 2008. IEEE Transaction.

      [6]. Neb Duric, Cuiping Li, Carri Glide-Hurst, Peter Littrup, Lianjie Huang and Jessica Lupinacc. Breast Imaging with Ultrasound Tomography: Clinical results at the Karmanos Cancer Institute. 2008. International Conference on BioMedical Engineering and Informatics.

      [7]. Poonguzhali.S and Ravindran.G. A complete automatic region growing method for segmentation of masses on ultrasound images. 2006. IEEE International Conference on BioMedical and pharmaceutical Engineering.

      [8]. Ruey-Feng Chang, Wen-Jie Wu, Chih-Chi Tseng, Dar-Ren Chen, and Woo Kyung Moon. 3-D snake for US in margin evaluation for malignant breast tumor excision using mammotome. 2003. IEEE transactions on Information TechnologyinBiomedicine, Vol. 25, No. 3.

      [9]. Simona Moldovanu, Luminita Moraru. Mass Detection and Classification in Breast Ultrasound Image Using K-means Clustering Algorithm.2010. IEEE. Vol. 25, No. 3.

      [10].KISHORE, P.V.V., KISHORE, S.R.C. and PRASAD, M.V.D., 2013. Conglomeration of hand shapes and texture information for recognizing gestures of indian sign language using feed forward neural networks. International Journal of Engineering and Technology, 5(5), pp. 3742-3756.

      [11].RAMKIRAN, D.S., MADHAV, B.T.P., PRASANTH, A.M., HARSHA, N.S., VARDHAN, V., AVINASH, K., CHAITANYA, M.N. and NAGASAI, U.S., 2015. Novel compact asymmetrical fractal aperture Notch band antenna. Leonardo Electronic Journal of Practices and Technologies, 14(27), pp. 1-12.

      [12].KARTHIK, G.V.S., FATHIMA, S.Y., RAHMAN, M.Z.U., AHAMED, S.R. and LAY-EKUAKILLE, A., 2013. Efficient signal conditioning techniques for brain activity in remote health monitoring network. IEEE Sensors Journal, 13(9), pp. 3273-3283.

      [13].KISHORE, P.V.V., PRASAD, M.V.D., PRASAD, C.R. and RAHUL, R., 2015. 4-Camera model for sign language recognition using elliptical fourier descriptors and ANN, International Conference on Signal Processing and Communication Engineering Systems - Proceedings of SPACES 2015, in Association with IEEE 2015, pp. 34-38.

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  • How to Cite

    R, C., R, S., & Poonguzhali, S. (2018). Automatic segmentation of multiple lesions in ultrasound breast image. International Journal of Engineering & Technology, 7(2.7), 665-670. https://doi.org/10.14419/ijet.v7i2.7.10919

    Received date: 2018-04-02

    Accepted date: 2018-04-02

    Published date: 2018-03-18