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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Health Organization (WHO).: Disease Incidence, Prevalence and Disability (2004)
Wondermom, N.: HealthBoards Message (2003). http://www.healthboards.com/boards/diabetes/39426-what-differance-between-glucose-intolerant-diabetes.html. Accessed 30 Oct 2017
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)
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)
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)
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)
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)
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)
Chen, H., Martin, B., Cai, H., et al.: Pancreas++: automated quantification of pancreatic islet cells in microscopy images. Front. Physiol. 3, 482 (2013)
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)
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)
Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101, 229–242 (2013)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bandyopadhyay, T., Mitra, S., Mitra, S., Das, N., Rato, L.M., Naskar, M.K. (2019). A Fast Algorithm for Automatic Segmentation of Pancreas Histological Images for Glucose Intolerance Identification. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_29
Download citation
DOI: https://doi.org/10.1007/978-981-13-1280-9_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1279-3
Online ISBN: 978-981-13-1280-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)