JACIII Vol.23 No.6 pp. 1073-1079
doi: 10.20965/jaciii.2019.p1073


FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images

Zhongming Luo*,†, Yu Zhang*, Zixuan Zhou*, Xuan Bi*, Haibin Wu*, and Zhentao Xin**

*The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province,
National Experimental Teaching Demonstration Center of Measuring and Control Technology, Harbin University of Science and Technology
No.52, Xuefu Road, Nangang District, Harbin, Heilongjiang 150080, China

**Department of Industry and Information of Heilongjiang Province
No.68, Heping Road, Nangang District, Harbin, Heilongjiang 150001, China

Corresponding author

June 4, 2019
July 26, 2019
November 20, 2019
micro-vessel, image segmentation, FCM clustering algorithm, level-set algorithm

To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-treated micro-vessel images were segmented by the fuzzy c-means (FCM) clustering algorithm to obtain original contours of interesting areas in images. By the evolution equations of the improved level set function, accurate segmentation of microscopic micro-vessel images was realized. This method can effectively solve the problem of manual initialization of contours, avoid the sensitivity to initialization and improve the accuracy of level-set segmentation. The experiment results indicate that compared with traditional micro-vessel image segmentation algorithms, this algorithm is of high efficiency, good noise immunity and accurate image segmentation.

Cite this article as:
Z. Luo, Y. Zhang, Z. Zhou, X. Bi, H. Wu, and Z. Xin, “FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.6, pp. 1073-1079, 2019.
Data files:
  1. [1] T. Zhang, C. Tang, and Z. Lei, “Multi-Scale Retinal Vessel Segmentation Based on Full Convolutional Neural Network,” Acta Optica Sinica, Vol.39, No.2, pp. 119-126, 2019 (in Chinese).
  2. [2] U. T. V. Nguyen, A. Bhuiyan, L. A. F. Park, and K. Ramamohanarao, “An effective retinal blood vessel segmentation method using multi-scale line detection,” Pattern Recognition, Vol.46, No.3, pp. 703-715, 2013.
  3. [3] A. M. Mendonca and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. on Medical Imaging, Vol.25, No.9, pp. 1200-1213, 2006.
  4. [4] C. Holbura, M. Gordan, A. Vlaicu, I. Stoian, and D. Capatana, “Retinal vessels segmentation using supervised classifiers decisions fusion,” Proc. of the 2012 IEEE Int. Conf. on Automation, Quality and Testing, Robotics, pp. 185-190, 2012.
  5. [5] M. M. Fraz, S. A. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, A. R. Rudnicka, and C. G. Owen, “An approach to localize the retinal blood vessels using bit planes and centerline detection,” Computer Methods and Programs in Biomedicine, Vol.108, No.2, pp. 600-616, 2012.
  6. [6] M. Zolkepli, F. Dong, and K. Hirota, “Visualizing Fuzzy Relationship in Bibliographic Big Data Using Hybrid Approach Combining Fuzzy c-Means and Newman-Girvan Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.6, pp. 896-907, 2014.
  7. [7] C. Huang, B. Yan, H. Jiang, and D. Wang, “MR Image Segmentation Based on Fuzzy C-Means Clustering and the Level Set Method,” Proc. of the 2008 5th Int. Conf. on Fuzzy Systems and Knowledge Discovery, Vol.1, pp. 67-71, 2008.
  8. [8] S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” J. of Computational Physics, Vol.79, No.1, pp. 12-49, 1988.
  9. [9] C. M. Li, C. Y. Xu, C. F. Gui, and M. D. Fox, “Level set evolution without re-initialization: A new variational formulation,” Proc. of the 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR’05), Vol.1, pp. 430-436, 2005.
  10. [10] P. S. Duth, C. A. Vipuldas, and V. P. Saikrishnan, “Integrated spatial fuzzy clustering with variational level set method for MRI brain image segmentation,” Proc. of the 2017 Int. Conf. on Communication and Signal Processing (ICCSP), pp. 1559-1562, 2017.
  11. [11] B. Dizdaroğlu, “Retinal vasculature segmentation based on fast level set method,” Proc. of the 2015 23nd Signal Processing and Communications Applications Conference (SIU), pp. 855-858, 2015.
  12. [12] X. Xu, S. Xu, L. Jin, and E. Song, “Characteristic analysis of Otsu threshold and its applications,” Pattern Recognition Letters, Vol.32, No.7, pp. 956-961, 2011.
  13. [13] K. Hung, S. Su, and Z. Lee, “Improving Ant Colony Optimization Algorithms for Solving Traveling Salesman Problems,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.4, pp. 433-442, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Feb. 17, 2020