Sine cosine optimization based multilevel segmentation of digital images

  • Authors

    • S Rakoth Kandan JAYAMUKHI INSTITUTE OF TECHNOLOGICAL SCIENCES
    • P Srinivas Rao JAYAMUKHI INSTITUTE OF TECHNOLOGICAL SCIENCES
    • B Durgalakshmi VIT UNIVERSITY
    2018-06-23
    https://doi.org/10.14419/ijet.v7i3.11774
  • Image Processing, Multilevel Segmentation, Optimization.
  • This main objective of this paper is to present a sine-cosine optimization algorithm for multilevel segmentation of real-time and medical images. It chooses the threshold values for all R, G, B channels of real life and medical images through effectively exploring the solution space in obtaining the global best solution. The results are compared with existing methods and finally, the proposed method is able to offer better segmentation results than that of an existing method.

     

     

  • References

    1. [1] Guo Dong and Ming Xie. (2005). Color Clustering and Learning for Image Segmentation Based on Neural Networks, IEEE Trans on Neural Networks, 16(4), 925- 935. https://doi.org/10.1109/TNN.2005.849822.

      [2] Tahir Sag and Mehmet Cunkas. (2015). Color image segmentation based on multi-objective artificial bee colony optimization, Applied Soft Computing, 34 (C), pp. 389-401. https://doi.org/10.1016/j.asoc.2015.05.016.

      [3] D. Welfer, J. Scharcanski, D.R. Marinho. (2010). A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images, Medical Imaging Graph, 34, 228 – 325. https://doi.org/10.1016/j.compmedimag.2009.10.001.

      [4] S. Ali, D. Sidibé, K.M. Adal, L. Giancardo, E. Chaum, T.P. Karnowski, F. Mériaudeau. (2013), Statistical atlas based exudate segmentation, Comput. Medical Imaging Graph, 37, 358–368. https://doi.org/10.1016/j.compmedimag.2013.06.006.

      [5] B. detection Harangi, by A. Hajdu. Fusing (2014). Automatic contours exudate detection by fusing multiple active contours and region wise classification, Comput. Biol. Med., 54, 156- 171.

      [6] Kavitha, D., Shenbaga Devi, S. (2005). Automatic detection of optic disc and exudates in retinal images, in: Proceedings 2005 International Conference on Intelligent sensing and information processing, 501-506. https://doi.org/10.1109/ICISIP.2005.1529506.

      [7] Foracchia, M., Grisan, E., Ruggeri, A. (2004). Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Transactions on Medical Imaging, 23, 1189–1195. https://doi.org/10.1109/TMI.2004.829331.

      [8] Madhusudhan M., Malay N., Nirmala S.R., Samerendra D. (2011) Image Processing Techniques for Glaucoma Detection. In: Abraham A., Mauri J.L.,Buford J.F., Suzuki J., Thampi S.M. (eds) Advances in Computing and Communications. ACC (2011). Communications in Computer and Information Science, Springer, Berlin, Heidelberg, 192, 365–373. https://doi.org/10.1007/978-3-642-22720-2_38.

      [9] D.J. Gawkrodger (2002), Dermatology: an illustrated colour text. Elsevier Health Sciences.

      [10] Mirjalili. S (2016), a Sine Cosine Algorithm for Solving Optimization Problems, Knowledge-Based Systems, 1-14.

      [11] Rakoth Kandan, S., and Sasikala Jayaraman, (2016). Self-Adaptive Dragonfly Based optimal Thresholding for Multilevel Segmentation of Digital Images, Journal of King Saud University - Computer and Information sciences.

  • Downloads

  • How to Cite

    Rakoth Kandan, S., Srinivas Rao, P., & Durgalakshmi, B. (2018). Sine cosine optimization based multilevel segmentation of digital images. International Journal of Engineering & Technology, 7(3), 1157-1160. https://doi.org/10.14419/ijet.v7i3.11774