Medical Image Segmentation Using Improved Affinity Propagation

  • Hong ZhuEmail author
  • Jinhui Xu
  • Junfeng Hu
  • Jing Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10149)


Affinity Propagation (AP) is an effective clustering method with a number of advantages over the commonly used k-means clustering. For example, it does not need to specify the number of clusters in advance, and can handle clusters with general topology, which makes it uniquely suitable for medical image segmentation as most of the objects in medical images are not roundly shaped. One factor hampering its applications is its relatively slow speed, especially for large-size images. To overcome this difficulty, we propose in this paper an Improved Affinity Propagation (IMAP) method with several improved features. Particularly, our IMAP method can adaptively select the key parameter p in AP according to the medical image gray histogram, and thus can greatly speed up convergence. Experimental results suggest that IMAP has a higher image entropy, lower class square error contrast, and shorter runtime than the AP algorithm.


Medical image segmentation Affinity propagation Gray level histogram 


  1. 1.
    Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16, 641–647 (1994)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C., Hall, L.O., Clarke, L.P.: Review of MR image segmentation techniques using pattern recognition. Med. Phys. 20, 1033–1048 (1993)CrossRefGoogle Scholar
  3. 3.
    Davatzikos, C., Bryan, R.N.: Using a deformable surface model to obtain a shape representation of the cortex. IEEE Trans. Med. Imaging 15, 785–795 (1996)CrossRefGoogle Scholar
  4. 4.
    Dueck, D., et al.: Constructing treatment portfolios using affinity propagation. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS, vol. 4955, pp. 360–371. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-78839-3_31 CrossRefGoogle Scholar
  5. 5.
    Foster, B., Bagci, U., Ziyue, X.: Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models. IEEE Trans. Biomed. Eng. 61, 711–724 (2014)CrossRefGoogle Scholar
  6. 6.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Gelenbe, E., Feng, Y., Krishnan, K.R.R.: Neural network methods for multispectral magnetic resonance images of brain using artificial neural networks. IEEE Trans. Med. Imaging 16, 911–918 (1997)CrossRefGoogle Scholar
  8. 8.
    Jia, S., Qian, Y., Ji, Z.: Band selection for hyperspectral imagery using affinity propagation. In: Proceedings of the 2008 Digital Image Computing: Techniques and Applications, Canberra, ACT, pp. 137–141. IEEE (2008)Google Scholar
  9. 9.
    Kelly, K.: Affinity program slashes computing times. Accessed 15 Dec 2007.–2952
  10. 10.
    Lee, L.K., Liew, S.C., Thong, W.J.: A review of image segmentation methodologies in medical image. In: Sulaiman, H.A., Othman, M.A., Othman, M.F.I., Rahim, Y.A., Pee, N.C. (eds.) Advanced Computer and Communication Engineering Technology. LNEE, vol. 315, pp. 1069–1080. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-07674-4_99 Google Scholar
  11. 11.
    Masood, S., Sharif, M., Masood, A., Yasmin, M., Raza, M.: A survey on medical image segmentation. Curr. Med. Imaging Rev. 11, 3–14 (2015)CrossRefGoogle Scholar
  12. 12.
    Ravindraiah, R., Tejaswini, K.: A survey of image segmentation algorithms based on fuzzy clustering. IJCSMC 2, 200–206 (2013)Google Scholar
  13. 13.
    Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survey of thresholding techniques. Comput. Vis. Graph. Image. Process. 41, 233–260 (1988)CrossRefGoogle Scholar
  14. 14.
    Sharma, N.J., Ray, A.K., Shukla, K.K.: Automated medical image segmentation techniques. J. Med. Phys. 35, 3–14 (2010)CrossRefGoogle Scholar
  15. 15.
    Xie, W., Qin, A.: The gray level resolution and intrinsic noise of human vision. Space Med. Med. Eng. 4, 51–55 (1991)MathSciNetGoogle Scholar
  16. 16.
    Zhang, X., Li, Y.: A medical image segmentation algorithm based on bi-directional region growing. Optik 126, 2398–2404 (2015)CrossRefGoogle Scholar
  17. 17.
    Zhen, D., Zhongshan, H., Jingyu, Y.: A image segmentation method base on Fuzzy c-means. Comput. Res. Dev. 7, 536–541 (1997)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Medical InformationXuzhou Medical CollegeXuzhouChina
  2. 2.Department of Computer Science and EngineeringState University of New York at BuffaloBuffaloUSA

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