Skip to main content
SpringerLink
Log in

Hierarchal Bayes model with AlexNet for characterization of M-FISH chromosome images

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The analysis of chromosomes is a significant and challenging task for clinical diagnosis and biological research. The technique based on color imaging is a multiplex fluorescent in situ hybridization (M-FISH), which was implemented to ease the exploration of the chromosomes. Thus, in this paper, we propose a novel quasi-Newton-based K-means clustering for the M-FISH image segmentation. Then, we use the expectation–maximization-based hierarchical Bayes model to characterize the M-FISH images. The contextual-based classification and region merging of chromosomal images is made to avoid any misclassification, and we made use of AlexNet, by modifying the activation functions of the sigmoid and softmax layer and for the optimum classification between the autosomal chromosomes and the sex chromosome. Finally, we conducted a performance analysis by measuring accuracy, recall, sensitivity, specificity, PPV, NPV, F-score, kappa, Jaccard, and Dice coefficient and compared with other existing methods and found that our proposed methodology can achieve more percentage of accuracy (6.96%) than the state of the art methods.

Graphical abstract

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Perkel JM (2019) “Chromosomal DNA comes into focus,” ed: NATURE PUBLISHING GROUP MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND

  2. Nino CL, Perez GF, Isaza N, Gutierrez MJ, Gomez JL, Nino G (2018) Characterization of sex-based DNA methylation signatures in the airways during early life. Sci Rep 8:1–10

    Article  CAS  Google Scholar 

  3. Onozato ML, Yapp C, Richardson D, Sundaresan T, Chahal V, Lee J et al (2019) Highly multiplexed fluorescence in situ hybridization for in situ genomics. J Mol Diagn 21:390–407

    Article  CAS  Google Scholar 

  4. Madabhushi A and Lee G (2016) “Image analysis and machine learning in digital pathology: challenges and opportunities,” ed: Elsevier

  5. Jang W, Chae H, Kim J, Son J-O, Kim SC, Koo BK et al (2016) Identification of small marker chromosomes using microarray comparative genomic hybridization and multicolor fluorescent in situ hybridization. Mol Cytogenet 9:61

    Article  Google Scholar 

  6. Sharma M, Jindal S, and Vig L (2020) “Method and system for automatic chromosome classification,” ed: Google Patents

  7. Lukumbuzya M, Schmid M, Pjevac P, Daims H (2019) A multicolor fluorescence in situ hybridization approach using an extended set of fluorophores to visualize microorganisms. Front Microbiol 10:1383

    Article  Google Scholar 

  8. Brich S, Bozzi F, Perrone F, Tamborini E, Cabras AD, Deraco M, et al (2019) “Fluorescence in situ hybridization (FISH) provides estimates of minute and interstitial BAP1, CDKN2A, and NF2 gene deletions in peritoneal mesothelioma”. Modern Pathol 1–11

  9. Patel SH, Batchala PP, Mrachek EKS, Lopes M-BS, Schiff D, Fadul CE et al (2020) MRI and CT identify isocitrate dehydrogenase (IDH)-mutant lower-grade gliomas misclassified to 1p/19q codeletion status with fluorescence in situ hybridization. Radiology 294:160–167

    Article  Google Scholar 

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

    Google Scholar 

  11. Ooi A, Inokuchi M, Horike S-I, Kawashima H, Ishikawa S, Ikeda H et al (2019) Amplicons in breast cancers analyzed by multiplex ligation-dependent probe amplification and fluorescence in situ hybridization. Hum Pathol 85:33–43

    Article  CAS  Google Scholar 

  12. Sangpakdee W, Phimphan S, Liehr T, Fan X, Pinthong K, Patawang I et al (2016) Characterization of chromosomal rearrangements in pileated gibbon (Hylobates pileatus) using multiplex-FISH technique. The Nucleus 59:131–135

    Article  Google Scholar 

  13. Ratan ZA, Zaman SB, Mehta V, Haidere MF, Runa NJ, and Akter N (2017) “Application of fluorescence in situ hybridization (FISH) technique for the detection of genetic aberration in medical science”. Cureus 9

  14. Qi G, Guan W, He Z, Huang A (2019) Adaptive kernel fuzzy C-Means clustering algorithm based on cluster structure. J Intell Fuzzy Syst 37:2453–2471

    Article  Google Scholar 

  15. Kapil S, Chawla M, and Ansari MD (2016) “On K-means data clustering algorithm with genetic algorithm,” in 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) 202–206

  16. Zeinalkhani L, Jamaat AA, Rostami K (2018) Diagnosis of brain tumor using combination of K-means clustering and genetic algorithm. Front Health Inform 7:6

    Google Scholar 

  17. Baheti B, Ahuja G, and Parode A (2016) “Automatic classification of M-FISH human chromosome images using fuzzy classifier and statistical classifier images using fuzzy classifier and statistical classifier,” in International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

  18. Menaka D and Vaidyanathan SG (2019) “Expectation maximization segmentation algorithm for classification of human genome image,” in 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) 1055–1059

  19. Qin Y, Wen J, Zheng H, Huang X, Yang J, Song N et al (2019) Varifocal-net: a chromosome classification approach using deep convolutional networks. IEEE Trans Med Imaging 38:2569–2581

    Article  Google Scholar 

  20. Esfahanian P and Akhavan M (2019) “GACNN: training deep convolutional neural networks with genetic algorithm,” arXiv preprint arXiv:1909.13354

  21. Sampat MP, Castleman K, and Bovik A (2002) “Pixel-by-pixel classification of MFISH images,” in Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society][Engineering in Medicine and Biology 999–1000

  22. Sampat MP, Bovik AC, Aggarwal JK, Castleman KR (2005) Supervised parametric and non-parametric classification of chromosome images. Pattern Recogn 38:1209–1223

    Article  Google Scholar 

  23. Choi H, Castleman KR, and Bovik AC (2004) “Joint segmentation and classification of M-FISH chromosome images,” in The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1636–1639

  24. Wang YP, Castleman KR (2005) Normalization of multicolor fluorescence in situ hybridization (M-FISH) images for improving color karyotyping. Cytom Part A J Int Soc Anal Cytol 64:101–109

    Article  Google Scholar 

  25. Karvelis PS, Fotiadis DI, Georgiou I, and Syrrou M (2006) “A watershed based segmentation method for multispectral chromosome images classification,” in 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3009-3012

  26. Schwartzkopf WC, Bovik AC, Evans BL (2005) Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images. IEEE Trans Med Imaging 24:1593–1610

    Article  Google Scholar 

  27. Wang Y-P and Dandpat AK (2006) “A hybrid approach of using wavelets and fuzzy clustering for classifying multispectral florescence in situ hybridization images”. Int J Biomed Imaging 2006

  28. Wang X, Zheng B, Li S, Zhang R, Mulvihill JJ, Chen WR, et al (2009) “Automated detection and analysis of fluorescent in situ hybridization spots depicted in digital microscopic images of Pap-smear specimens”. J Biomed Optics 14:021002

  29. Choi H, Bovik AC, Castleman KR (2008) Feature normalization via expectation maximization and unsupervised nonparametric classification for M-FISH chromosome images. IEEE Trans Med Imaging 27:1107–1119

    Article  Google Scholar 

  30. Karvelis PS, Likas AC (2013) Fully unsupervised m-FISH chromosome image characterization. IEEE J Biomed Health Inform 17:1068–1078

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan College of Engineering, Coimbatore, Tamil Nadu, India

    V. S. Kanimozhi

  2. Department of Electronics and Communication Engineering, Info Institute of Engineering, Coimbatore, Tamil Nadu, India

    M. Balasubramani

  3. Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India

    R. Anuradha

Authors
  1. V. S. Kanimozhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Balasubramani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Anuradha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. S. Kanimozhi.

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

Kanimozhi, V.S., Balasubramani, M. & Anuradha, R. Hierarchal Bayes model with AlexNet for characterization of M-FISH chromosome images. Med Biol Eng Comput 59, 1529–1544 (2021). https://doi.org/10.1007/s11517-021-02384-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-021-02384-0

Keywords

Search

Navigation