Advertisement

A Fast Algorithm for Automatic Segmentation of Pancreas Histological Images for Glucose Intolerance Identification

  • Tathagata Bandyopadhyay
  • Shyamali Mitra
  • Sreetama Mitra
  • Nibaran DasEmail author
  • Luis Miguel Rato
  • Mrinal Kanti Naskar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 740)

Abstract

This paper describes a novel fast algorithm for automatic segmentation of islets of Langerhans and β-cell region from pancreas histological images for automatic identification of glucose intolerance. Here, LUV colour space and connected component analysis are used on 134 images among which 56 are of normal and rest 78 are of prediabetic type. The paper also talks about a supervised learning approach for classifying the images based on their morphological features. In the present work, we have introduced a modern classifier weighted ELM (Extreme Learning Machine) for prediabetes identification. Performances of weighted ELM are comparable with all the present-day’s robust classifiers such as Support Vector Machines (SVM), Multilayer Perceptron (MLP), etc. We have also compared the result with traditional ELM and observed better performance in the present skewed dataset with substantial improvement in training time.

Keywords

Automatic segmentation Histological image Islets of Langerhans β-cell Diabetes Computerized diagnostic system Extreme learning machine 

Notes

Acknowledgements

The authors thank Professor Fernando Capela e Silva, from the Department of Biology and Ana R. Costa and Célia M. Antunes, from the Department of Chemistry, University of Évora, Portugal, for the dataset used in this article.

References

  1. 1.
    World Health Organization (WHO).: Disease Incidence, Prevalence and Disability (2004)Google Scholar
  2. 2.
  3. 3.
    Bandyopadhyay, T., Mitra, S., Mitra, S., et al.: Analysis of pancreas histological images for glucose intolerance identification using wavelet decomposition. In: Satapathy, S.C., Bhateja, V., Udgata, S.K., Pattnaik, P.K. (eds.) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications : FICTA 2016, vol. 1, pp 653–661. Springer Singapore, Singapore (2017)Google Scholar
  4. 4.
    Kakimoto, T., Kimata, H., Iwasaki, S., et al.: Automated recognition of pancreatic islets in Zucker diabetic fatty rats treated with exendin-4. J. Endocrinol. 216, 1–24 (2012)CrossRefGoogle Scholar
  5. 5.
    Rato, L.M., e Silva, F.C., Costa, A.R., Antunes, C.M.: Analysis of pancreas histological images for glucose intolerance identification using imagej—preliminary results. In: 4th Eccomas Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE), pp 319–322. CRC Press (2013)Google Scholar
  6. 6.
    Rojo, M.G., Bueno, G., Slodkowska, J.: Review of imaging solutions for integrated quantitative immunohistochemistry in the pathology daily practice. Folia Histochem. Cytobiol. 47, 349–354 (2009)Google Scholar
  7. 7.
    Prasad, K., Prabhu, G.K.: Image analysis tools for evaluation of microscopic views of immunohistochemically stained specimen in medical research—a review. J. Med. Syst. 36, 2621–2631 (2012)CrossRefGoogle Scholar
  8. 8.
    Isse, K., Lesniak, A., Grama, K., et al.: Digital transplantation pathology: combining whole slide imaging, multiplex staining and automated image analysis. Am. J. Transplant. 12, 27–37 (2012)CrossRefGoogle Scholar
  9. 9.
    Chen, H., Martin, B., Cai, H., et al.: Pancreas++: automated quantification of pancreatic islet cells in microscopy images. Front. Physiol. 3, 482 (2013)CrossRefGoogle Scholar
  10. 10.
    Berclaz, C., Goulley, J., Villiger, M., et al.: Diabetes imaging—quantitative assessment of islets of Langerhans distribution in murine pancreas using extended-focus optical coherence microscopy. Biomed. Opt. Expr. 3, 1365–1380 (2012)CrossRefGoogle Scholar
  11. 11.
    Aswathy, M.A., Jagannath, M.: Detection of breast cancer on digital histopathology images: present status and future possibilities. Inf. Med. Unlocked 8, 74–79 (2017)CrossRefGoogle Scholar
  12. 12.
    Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101, 229–242 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Tathagata Bandyopadhyay
    • 1
  • Shyamali Mitra
    • 2
  • Sreetama Mitra
    • 1
  • Nibaran Das
    • 4
    Email author
  • Luis Miguel Rato
    • 3
  • Mrinal Kanti Naskar
    • 2
  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia
  2. 2.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  3. 3.Department of InformaticsUniversity of EvoraEvoraPortugal
  4. 4.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations