Soft Computing

, Volume 21, Issue 3, pp 611–625 | Cite as

Segmentation of carbon nanotube images through an artificial neural network

  • María Celeste Ramírez Trujillo
  • Teresa E. Alarcón
  • Oscar S. Dalmau
  • Adalberto Zamudio Ojeda
Focus
  • 143 Downloads

Abstract

Segmentation of carbon nanotube images is an important task for nanotechnology. The segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work, we propose two segmentation algorithms for carbon nanotube images. Each algorithm includes three stages: preprocessing, segmentation and postprocessing. The first one is applied on images from scanning electron microscopy and employs a matched filter bank in the preprocessing step followed by a neural network in the segmenting phase. The second algorithm uses the Perona–Malik filter for enhancing the nanotube information. The segmentation phase is composed of the relaxed Otsu’s threshold and an artificial neural network. This algorithm is applied on images from transmission electron microscopy. The postprocessing stage, for both algorithms, is based on mathematical morphology. The performance of the proposed algorithms is numerically evaluated by using real image databases, manually segmented by an expert. The algorithm for segmentation of scanning electron microscopy achieved 92.74% of overall accuracy, while the algorithm for segmentation of transmission electron microscopy obtained an accuracy of 73.99% if the whole image is considered. A performance improvement is accomplished if only the region of interest is segmented, arriving to 84.19% of overall accuracy.

Keywords

Segmentation Artificial neural network Filter bank Thresholding 

References

  1. Aguilar O, Alarcón T, Dalmau O, Zamudio A (2014) Characterization of nanotube structures using digital-segmented images. In: 2014 13th Mexican international conference on artificial intelligence (MICAI), IEEE, pp 57–65Google Scholar
  2. Batenburg KJ, Sijbers J (2009) Adaptive thresholding of tomograms by projection distance minimization. Pattern Recogn 42(10):2297–2305CrossRefMATHGoogle Scholar
  3. Cao L-L, Huang W-B, Sun F-C (2014) Optimization-based extreme learning machine with multi-kernel learning approach for classification. 2014 22nd International conference on pattern recognition (ICPR)Google Scholar
  4. Celeste RTM, Dalmau OS, Alarcón TE, Zamudio Ojeda A (2015) Segmentation of carbon nanotube images through an artificial neural network. Springer, New York, pp 338–350Google Scholar
  5. Chaku P, Shah P, Bakshi S (2014) A digital image processing algorithm for automation of human karyotyping. Int J Comput Sci Commun 5(1):54–56Google Scholar
  6. Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imag 8(3):263–269CrossRefGoogle Scholar
  7. Chuang K-S, Tzeng H-L, Chen S, Wu J, Chen T-J (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imag Graph 30:9–15CrossRefGoogle Scholar
  8. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATHGoogle Scholar
  9. Dalmau O, Alarcon T (2011) MFCA: matched filters with cellular automata for retinal vessel detection. Springer, BerlinGoogle Scholar
  10. de Jesús Guerrero J, Dalmau O, Alarcón TE, Zamudio A (2014) Frequency filter bank for enhancing carbon nanotube images. Springer International Publishing, ChamCrossRefGoogle Scholar
  11. Fu H, Xu Y, Wong DWK, Liu J (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI), pp 698–701Google Scholar
  12. Geman S, Geman D (1984) Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE transactions on pattern analysis and machine intelligence. PAMI-6(6):721–741Google Scholar
  13. Gonzalez R, Woods R (2008) Digital image processing. Pearson/Prentice Hall, Upper Saddle RiverGoogle Scholar
  14. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621CrossRefGoogle Scholar
  15. Kang F, Han S, Salgado R, Li J (2015) System probabilistic stability analysis of soil slopes using gaussian process regression with latin hypercube sampling. Comput Geotech 63:13–25. http://www.sciencedirect.com/science/article/pii/S0266352X1400161X
  16. Kang F, Li J-S, Wang Y, Li J (2016a) Extreme learning machine-based surrogate model for analyzing system reliability of soil slopes. Eur J Environ Civil Eng, 1–22Google Scholar
  17. Kang F, Li JS, Li JJ (2016b) System reliability analysis of slopes using least squares support vector machines with particle swarm optimization. Neurocomputing 209:46–56. Advances in computational intelligence with internet of things. http://www.sciencedirect.com/science/article/pii/S0925231216305859
  18. Kang F, Xu Q, Li J (2016) Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Modell 40(11–12):6105–6120. http://www.sciencedirect.com/science/article/pii/S0307904X16300464
  19. Karan SK, Samadder SR (2016) Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas. Environ Monit Assess 188(8):1–13. http://dx.doi.org/10.1007/s10661-016-5494-x
  20. Lindeberg T, Li M-X (1997) Segmentation and classification of edges using minimum description length approximation and complementary junction cues. Comput Vis Image Underst 67(1):88–98CrossRefGoogle Scholar
  21. Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imag PP(99):1–1Google Scholar
  22. Nock R, Nielsen F (2004) Statistical region merging. IEEE Trans Pattern Anal Mach Intell 26(11):1452–1458CrossRefGoogle Scholar
  23. Oliveira WS, Teixeira JV, Ren TI, Cavalcanti GDC, Sijbers J (2016) Unsupervised retinal vessel segmentation using combined filters. PLoS ONE 11(2):1–21Google Scholar
  24. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66Google Scholar
  25. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  26. Plodpradista P, Keller JM, Popescu M (2015) An application of log-gabor filter on road detection in arid environments for forward looking buried object detection. In: Proceedings of SPIE, vol 9454, pp 94540R–94540R–11Google Scholar
  27. Rajaraman S, Vaidyanathan G, Chokkalingam A (2013) Segmentation and removal of interphase cells from chromosome images using multidirectional block ranking. Int J Bio Sci Bio Technol 5(3):79–91Google Scholar
  28. Rodner E, Freytag A, Bodesheim P, Fröhlich B, Denzler J (2016) Large-scale gaussian process inference with generalized histogram intersection kernels for visual recognition tasks. Int J Comput Vis 00:1–28. http://dx.doi.org/10.1007/s11263-016-0929-y
  29. Shapiro LG, Stockman GC (2001) Computer vision. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  30. Torres AD (1996) Procesamiento digital de imágenes. Perf Educ 72:1–15Google Scholar
  31. Wortmann T, Fatikow S (2009) Carbon nanotube detection by scanning electron microscopy. In; MVA, pp 370–373Google Scholar
  32. Zhang D, Zhao Y (2016) Novel accurate and fast optic disc detection in retinal images with vessel distribution and directional characteristics. IEEE J Biomed Health Inf 20(1):333–342MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Universidad de GuadalajaraGuadalajaraMexico
  2. 2.Centro de Investigación en MatemàticasGuanajuatoMexico

Personalised recommendations