Skip to main content
Log in

An improved denoising of G-banding chromosome images using cascaded CNN and binary classification network

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Background

Chromosome analysis plays an important role in detecting genetic disorders. However, it is time-consuming when it is done manually. The first step for an automated solution is removing the background noise in the chromosome images. Denoising is studied by many researchers; however, it is still a challenging task due to contrast issues, blotches, and non-chromosome objects.

Methods

In this paper, we proposed a cascaded neural network architecture for denoising G-banding chromosomes images. The proposed method consists of two steps. The first step is the initial segmentation network which combines the capabilities of U-net and residual units. The second step is the classification block, which is implemented in order to automate the denoising process and reduce the pixel losses on the chromosomes.

Results

The results showed that the proposed segmentation network achieves a higher dice score compared to state-of-the-art semantic segmentation neural networks, and the classification block greatly reduces the pixel losses on the chromosomes. We tested the proposed denoising method on 84 G-banding chromosome images and achieved a 98.74% dice score.

Conclusion

Our automated denoising method outperformed the methods presented in previous studies and state-of-the-art methods. It can help cytogeneticists with repetitive work and provide them more accurate chromosomes for further evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Graham, J., Piper, J.: Automatic karyotype analysis 141–185,(1994)

  2. Ji, L.: Intelligent splitting in the chromosome domain. Pattern Recogn. 22(5), 519–532 (1989). https://doi.org/10.1016/0031-3203(89)90021-6

    Article  Google Scholar 

  3. Guimaraes, L., Schuck, A., Elbern, A.: Chromosome classification for karyotype composing applying shape representation on wavelet packet transform. Pattern Recogn. Lett. 1, 941–943 (2003)

    Google Scholar 

  4. Karvelis, P., Fotiadis, D., Syrrou, M., Georgiou, I.: Segmentation of chromosome images based on a recursive watershed transform. Conf. Proc. IEEE Eng. Med. Biol. Soc. 11, 1727–1983 (2005)

  5. Srisang, W., Jaroensutasinee, K., Jaroensutasinee, M.: Segmentation of overlapping chromosome images using computational geometry. WJST 3(2), 181–194 (2006)

    Google Scholar 

  6. Arachchige, A.S., Samarabandu, J., Knoll, J., Khan, W., Rogan, P.: An image processing algorithm for accurate extraction of the centerline from human metaphase chromosomes 3613–3616,(2010)

  7. Wayalun, P., Chomphuwiset, P., Laopracha, N., Wanchanthuek, P.: Images enhancement of g-band chromosome using histogram equalization, otsu thresholding, morphological dilation and flood fill techniques 163–168 (2012)

  8. Arora, T., Dhir, R.: An efficient segmentation method for overlapping chromosome images. Int. J. Comput. Appl. 95, 29–32 (2014). https://doi.org/10.5120/16560-4861

    Article  Google Scholar 

  9. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  10. Ji, L.: Fully automatic chromosome segmentation. Cytom. J. Int. Soc. Anal. Cytol. 17(3), 196–208 (1994)

    Google Scholar 

  11. Stanley, R.J., Keller, J.M., Gader, P., Caldwell, C.W.: Data-driven homologue matching for chromosome identification. IEEE Trans. Med. Imaging 17(3), 451–462 (1998)

    Article  Google Scholar 

  12. Grisan, E., Poletti, E., Ruggeri, A.: Automatic segmentation and disentangling of chromosomes in q-band prometaphase images. IEEE Trans. Inf. Technol. Biomed. 13(4), 575–581 (2009)

    Article  Google Scholar 

  13. Sugapriyaa, T., Kaviyapriya, P., Gomathi, P.: Segmentation and extraction of chromosomes from g-band metaphase images. Indian J. Sci. Technol. 11(18), 1–5 (2018)

    Article  Google Scholar 

  14. Lerner, B., Guterman, H., Dinstein, I.: A classification-driven partially occluded object segmentation (cpoos) method with application to chromosome analysis. IEEE Trans. Signal Process. 46(10), 2841–2847 (1998)

    Article  Google Scholar 

  15. Lerner, B.: Toward a completely automatic neural-network-based human chromosome analysis. IEEE Trans. Syst. Man Cybernet. Part B (Cybernetics) 28(4), 544–552 (1998)

  16. Soumya, D., Arya, V.: Chromosome segmentation using k-means clustering. Int. J. Sci. Eng. Res. 4(9), 937–940 (2013)

    Google Scholar 

  17. Uhlmann, V., Delgado-Gonzalo, R., Unser, M., Michel, P., Baldi, L., Wurm, F.: User-friendly image-based segmentation and analysis of chromosomes 395–398 (2016)

  18. Shen, X., Qi, Y., Ma, T., Zhou, Z.: A dicentric chromosome identification method based on clustering and watershed algorithm. Scientific Reports 9(1), 1–11 (2019)

    Google Scholar 

  19. Cao, H., Wang, Y.-P.: Segmentation of m-fish images for improved classification of chromosomes with an adaptive fuzzy c-means clustering algorithm 1442–1445,(2011)

  20. Dougherty, A.W., You, J.: A kernel-based adaptive fuzzy c-means algorithm for m-fish image segmentation 198–205,(2017)

  21. Poletti, E., Zappelli, F., Ruggeri, A., Grisan, E.: A review of thresholding strategies applied to human chromosome segmentation. Comput. Methods Prog. Biomed. 108(2), 679–688 (2012)

    Article  Google Scholar 

  22. Kapur, J.N., Sahoo, P.K., Wong, A.K.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985)

    Article  Google Scholar 

  23. Yan, F., Zhang, H., Kube, C.R.: A multistage adaptive thresholding method. Pattern Recogn. Lett. 26(8), 1183–1191 (2005)

    Article  Google Scholar 

  24. Grisan, E., Poletti, E., Ruggeri, A.: An improved segmentation of chromosomes in q-band prometaphase images using a region based level set 748–751,(2009)

  25. Andrade, M.F., Cordeiro, F.R., Macário, V., Lima, F.F., Hwang, S.F., Mendonça, J.C.: A fuzzy-adaptive approach to segment metaphase chromosome images 290–295,(2018)

  26. Andrade, M.F., Dias, L.V., Macario, V., Lima, F.F., Hwang, S.F., Silva, J.C., Cordeiro, F.R.: A study of deep learning approaches for classification and detection chromosomes in metaphase images. Mach. Vis. Appl. 31(7), 1–18 (2020)

    Google Scholar 

  27. Mona, A.A., Shaalan, M., Hassanien, A.E., Kim, T.-H.: A simple approach for segmentation and removal of interphase cells from chromosome images 3–8,(2015)

  28. Jahani, S., Setarehdan, S.K., Fatemizadeh, E.: Automatic identification of overlapping/touching chromosomes in microscopic images using morphological operators 1–4,(2011)

  29. Yilmaz, I.C., Jie, Y., Altinsoy, E., Lei, Z.: An improved segmentation for raw g-band chromosome images 944–950,(2018)

  30. Altinsoy, E., Yang, J., Yilmaz, C.: Fully-automatic raw g-band chromosome image segmentation. IET Image Process. 14(9), 1920–1928 (2020)

    Article  Google Scholar 

  31. Altinsoy, E., Yilmaz, C., Wen, J., Wu, L., Yang, J., Zhu, Y.: Raw g-band chromosome image segmentation using u-net based neural network 117–126,(2019)

  32. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation, pp. 234–241 (2015)

  33. Bai, H., Zhang, T., Lu, C., Chen, W., Xu, F., Han, Z.-B.: Chromosome extraction based on u-net and yolov3. IEEE Access 8, 178563–178569 (2020)

    Article  Google Scholar 

  34. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks 630–645,(2016)

  35. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation 3431–3440,(2015)

  36. Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation 179–187,(2016)

  37. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition 770–778,(2016)

  38. Minaee, S., Fotouhi, M., Khalaj, B.H.: A geometric approach to fully automatic chromosome segmentation 1–6,(2014)

  39. Zou, K.H., Warfield, S.K., Bharatha, A., Tempany, C.M., Kaus, M.R., Haker, S.J., Wells, W.M., III., Jolesz, F.A., Kikinis, R.: Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acad. Radiol. 11(2), 178–189 (2004)

  40. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: an imperative style, high-performance deep learning library, pp. 8024–8035 (2019)

  41. Bradski, G.: The OpenCV Library, Dr. Dobb’s Journal of Software Tools (2000)

  42. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)

  43. Ruder, S.: An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747 (2016)

  44. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation 3–11 (2018)

  45. Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y, Kainz, B. et al.: Attention u-net: Learning where to look for the pancreas, arXiv e-prints arXiv:1804.03999 (2018)

  46. Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., Liu, J.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)

    Article  Google Scholar 

  47. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  48. Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation, arXiv e-prints arXiv:1606.00915 (2017)

  49. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network 2881–2890 (2017)

Download references

Acknowledgements

The authors thank the Center for Medical Genetics, School of Life Sciences, Central South University, and Diagens Hangzhou for providing the chromosome images and manual karyotypes. This research was supported by the Committee of Science and Technology, Shanghai, China (no. 19510711200). The authors are grateful to the anonymous reviewers for their helpful comments.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jie Yang or Enmei Tu.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Code availability

The source code is available on https://github.com/emrecanaltinsoy/chromosome-semantic-segmentation.

CRediT authorship contribution statement

Emrecan Altinsoy had contributed to conceptualization, methodology, formal analysis and investigation, writing the original draft preparation, writing, reviewing, and editing, and data curation. Jie Yang took part in supervision, writing, reviewing, and editing, and funding acquisition. Enmei Tu was involved in supervision, and writing, reviewing, and editing

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Altinsoy, E., Yang, J. & Tu, E. An improved denoising of G-banding chromosome images using cascaded CNN and binary classification network. Vis Comput 38, 2139–2152 (2022). https://doi.org/10.1007/s00371-021-02273-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02273-5

Keywords

Navigation