Automatic Classification of Low-Resolution Chromosomal Images

  • Swati Swati
  • Monika SharmaEmail author
  • Lovekesh Vig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)


Chromosome karyotyping is a two-staged process consisting of segmentation followed by pairing and ordering of 23 pairs of human chromosomes obtained from cell spread images during metaphase stage of cell division. It is carried out by cytogeneticists in clinical labs on the basis of length, centromere position, and banding pattern of chromosomes for the diagnosis of various health and genetic disorders. The entire process demands high domain expertise and considerable amount of manual effort. This motivates us to automate or partially automate karyotyping process which would benefit and aid doctors in the analysis of chromosome images. However, the non-availability of high resolution chromosome images required for classification purpose creates a hindrance in achieving high classification accuracy. To address this issue, we propose a Super-Xception network which takes the low-resolution chromosome images as input and classifies them to one of the 24 chromosome class labels after conversion into high resolution images. In this network, we integrate super-resolution deep models with standard classification networks e.g., Xception network in our case. The network is trained in an end-to-end manner in which the super-resolution layers help in conversion of low-resolution images to high-resolution images which are subsequently passed through deep classification layers for label assigning. We evaluate our proposed network’s efficacy on a publicly available online Bioimage chromosome classification dataset of healthy chromosomes and benchmark it against the baseline models created using traditional deep convolutional neural network, ResNet-50 and Xception network.


Low resolution chromosomes Karyotyping Chromosome classification Super-Xception Super-ResNet 


  1. 1.
    Bioimage chromosome classification: Dataset online.
  2. 2.
    Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)Google Scholar
  3. 3.
    Agam, G., Dinstein, I.: Geometric separation of partially overlapping nonrigid objects applied to automatic chromosome classification. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1212–1222 (1997)CrossRefGoogle Scholar
  4. 4.
    Al-Rfou, R., et al.: Theano: a python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688 472, 473 (2016)
  5. 5.
    Balaji, V.S., Vidhya, S.: Separation of touching and overlapped human chromosome images. In: Gupta, S., Bag, S., Ganguly, K., Sarkar, I., Biswas, P. (eds.) Advancements of Medical Electronics. Lecture Notes in Bioengineering. Springer, New Delhi (2015). Scholar
  6. 6.
    Britto, A.P., Ravindran, G.: A review of cytogenetics and its automation. J. Med. Sci. 7, 1–18 (2007)CrossRefGoogle Scholar
  7. 7.
    Cai, D., Chen, K., Qian, Y., Kämäräinen, J.K.: Convolutional low-resolution fine-grained classification. Pattern Recognit. Lett. (2017)Google Scholar
  8. 8.
    Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv preprint (2016)Google Scholar
  9. 9.
    Chollet, F., et al.: Keras (2015)Google Scholar
  10. 10.
    Dai, D., Timofte, R., Van Gool, L.: Jointly optimized regressors for image super-resolution. In: Computer Graphics Forum, vol. 34, pp. 95–104. Wiley Online Library (2015)Google Scholar
  11. 11.
    Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)CrossRefGoogle Scholar
  12. 12.
    Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). Scholar
  13. 13.
    Errington, P.A., Graham, J.: Application of artificial neural networks to chromosome classification. Cytom. Part A 14(6), 627–639 (1993)CrossRefGoogle Scholar
  14. 14.
    Fattal, R.: Image upsampling via imposed edge statistics. In: ACM Transactions on Graphics (TOG), vol. 26, p. 95. ACM (2007)Google Scholar
  15. 15.
    Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. (TOG) 30(2), 12 (2011)CrossRefGoogle Scholar
  16. 16.
    Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 349–356. IEEE (2009)Google Scholar
  17. 17.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  18. 18.
    Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2015)Google Scholar
  19. 19.
    Huang, J., Mumford, D.: Statistics of natural images and models. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 541–547. IEEE (1999)Google Scholar
  20. 20.
    Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graph. Models Image Process. 53, 231–239 (1991)Google Scholar
  21. 21.
    Jahani, S., Setarehdan, S.K.: An automatic algorithm for identification and straightening images of curved human chromosomes. Biomed. Eng. Appl. Basis Commun. 24(06), 503–511 (2012)CrossRefGoogle Scholar
  22. 22.
    Javan-Roshtkhari, M., Setarehdan, S.K.: A new approach to automatic classification of the curved chromosomes. In: 5th International Symposium on Image and Signal Processing and Analysis, ISPA 2007, pp. 19–24. IEEE (2007)Google Scholar
  23. 23.
    Jindal, S., Gupta, G., Yadav, M., Sharma, M., Vig, L.: Siamese networks for chromosome classification. In: The IEEE International Conference on Computer Vision (ICCV), October 2017Google Scholar
  24. 24.
    Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)Google Scholar
  25. 25.
    Kim, J., Kwon Lee, J., Mu Lee, K.: Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637–1645 (2016)Google Scholar
  26. 26.
    Lerner, B., Levinstein, M., Rosenberg, B., Guterman, H., Dinstein, L., Romem, Y.: Feature selection and chromosome classification using a multilayer perceptron neural network. In: IEEE International Conference on Neural Networks, vol. 6, pp. 3540–3545 (1994)Google Scholar
  27. 27.
    Lerner, B.: Toward a completely automatic neural-network-based human chromosome analysis. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 28, 544–552 (1998)CrossRefGoogle Scholar
  28. 28.
    Madian, N., Jayanthi, K.: Overlapped chromosome segmentation and separation of touching chromosome for automated chromosome classification. In: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5392–5395 (2012)Google Scholar
  29. 29.
    Poletti, E., Grisan, E., Ruggeri, A.: Automatic classification of chromosomes in Q-band images. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, pp. 1911–1914. IEEE (2008)Google Scholar
  30. 30.
    Schulter, S., Leistner, C., Bischof, H.: Fast and accurate image upscaling with super-resolution forests. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3791–3799 (2015)Google Scholar
  31. 31.
    Sharma, M., Saha, O., Sriraman, A., Vig, L., Hebbalaguppe, R., Karande, S.: Crowdsourcing for chromosome segmentation and deep classification. In: CVPR 2017. IEEE CVPR 2017 (2017)Google Scholar
  32. 32.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014).
  33. 33.
    Szegedy, C., et al.: Going deeper with convolutions. CoRR abs/1409.4842 (2014),
  34. 34.
    Yang, C.-Y., Ma, C., Yang, M.-H.: Single-image super-resolution: a benchmark. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 372–386. Springer, Cham (2014). Scholar
  35. 35.
    Yang, J., Lin, Z., Cohen, S.: Fast image super-resolution based on in-place example regression. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1059–1066. IEEE (2013)Google Scholar
  36. 36.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. CoRR abs/1311.2901 (2013).
  37. 37.
    Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., et al. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.TCS ResearchNew DelhiIndia

Personalised recommendations