Advertisement

Classification of Nuclei in Follicular Lyphoma Tissue Sections Using Different Stains and Bayesian Networks

  • Kosmas Dimitropoulos
  • Panagiotis Barmpoutis
  • Triantafyllia Koletsa
  • Ioannis Kostopoulos
  • Nikos GrammalidisEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

Automated centroblast (CB) detection in Follicular Lymphoma (FL) tissue samples has recently attracted significant research interest. Most of the methods described in the literature are based on the use of Hematoxilin and Eosin (H&E) stain. However, the automated detection of CBs from H&E stained images remains a challenging issue. To this end, this paper presents a novel approach which is based on the use of both PAX5 and H&E stains in tissue sections sliced at the thickness of 1μm. The goal of PAX5 is three-fold: to facilitate the segmentation of nuclei, to remove a number of follicular dendritic cells and finally to extract morphological characteristics of nuclei. Furthermore, the use of H&E stain enables us to extract textural information related to histological characteristics used by pathologists in diagnosis of FL grading. In our method we propose a novel algorithm for the separation of overlapped nuclei inspired by the clustering of large scale visual vocabularies. Finally, aiming to model pathologists’ knowledge used in FL grading, we use a Bayesian Network classifier to combine the morphological and textural characteristics. Experiments conducted on a dataset of ten pairs of PAX5 and H&E images demonstrate the potential of the proposed approach providing an average detection rate of 93.46%.

Keywords

Biomedical image processing Follicular Lymphoma Multi-stains Cell segmentation Overlapped nuclei segmentation Bayesian Networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S H Swerdlow et al., Classification of Tumours of Haemaopoietic and Lymphoid Tissues, Fourth Edition ed. Lyon: World Health Organization, 2008.Google Scholar
  2. 2.
    R B Mann and C W Berard, “Criteria for the cytologic subclassification of follicular lymphomas: a proposed alternative method,” Hematl. Oncol., vol. 1, no. 2, pp. 187-192, 1983.Google Scholar
  3. 3.
    A Freedman, “Follicular Lymphoma: 2012 Update on Diagnosis and Managemet,” Am. J. Hematol., vol. 87, no. 10, pp. 988-995, September 2012.Google Scholar
  4. 4.
    O Sertel et al., “Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading,” Signal Processing Systems, vol. 55, no. 1-3, pp. 169-183, April 2009.Google Scholar
  5. 5.
    K Belkacem-Boussaid, O Sertel, G Lozanski, A Ahana’aah, and M Gurcan, “Extraction of color features in the spectral domain to recognize centroblasts in histopathology,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, USA, 2009.Google Scholar
  6. 6.
    O Seterl, G Lozanski, A Shana’ah, and M Gurcan, “Computer aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation,” IEEE Trans. Biomed. Eng., vol. 57, no. 10, pp. 2613-2616, 2010.Google Scholar
  7. 7.
    E N Kornaropoulos, M Niazi, G Lozanski, and M N Gurcan, “Histopathological image analysis for centroblasts classification through dimensionality reduction approaches,” Cytometry Part A, vol. 85, no. 3, pp. 242-255, 2014.Google Scholar
  8. 8.
    Y Song et al., “Unsupervised content classification based non-rigid registration of differently stained histology images,” IEEE Trans. Biomed. Eng., vol. 96, no. 1, pp. 96-108, Janouary 2014.Google Scholar
  9. 9.
    M Horcher, A Souabni, and M Busslinger, “Pax5/BSAP Maintains the Identity of B Cells in Late B Lymphopoiesis,” Immunity, vol. 14, pp. 779-790, 2001.Google Scholar
  10. 10.
    K C Jensen et al., “The utility of PAX5 immunohistochemistry in the diagnosis of undifferentiated malignant neoplasms,” Modern Pathology, vol. 20, no. 8, pp. 871-877, August 2007.Google Scholar
  11. 11.
    Y Boykov, O Veksler, and R Zabih, “Fast Approximate Energy Minimization via Graph Cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222-1239, 2001.Google Scholar
  12. 12.
    P Soille, Morphological Image Analysis: Principles and Applications. Berlin, Germany: Springer-Verlag, 1999.Google Scholar
  13. 13.
    Y Avrithis and Y Kalantidis, “Approximate Gaussian Mixtures for Large Scale Vocabularies,” in European Conference on Computer Vision, Florence, Italy, 2012.Google Scholar
  14. 14.
    Mohamed Bouguessa, Shengrui Wang, and Haojun Sun, “An objective approach to cluster validation,” Pattern Recognition Letters, vol. 27, no. 13, pp. 1419-1430, 2006.Google Scholar
  15. 15.
    Jos B.T.M. Roerdink and Arnold Meijster, “The Watershed Transform: Definitions, Algorithms and Parallelization Strategies,” Fundamenta Informaticae, vol. 41, pp. 187-228, 2001.Google Scholar
  16. 16.
    W Gander, G H Golub, and R Strebel, “Least-square fitting of circles and ellipses,” BIT, vol. 34, no. 4, pp. 558-578, 1994.Google Scholar
  17. 17.
    Kosmas Dimitropoulos, Emmanouil Michail, Triantafyllia Koletsa, Ioannis Kostopoulos, and Nikos Grammalidis, “Using adaptive neuro-fuzzy inference systems for the detection of centroblasts in microscopic images of follicular lymphoma,” Signal, Image and Video Processing, vol. 8, no. 1, pp. 33-40, December 2014.Google Scholar
  18. 18.
    P Kevin Murphy, “The Bayes Net Toolbox for MATLAB,” Computing Science and Statistics, vol. 33, 2001.Google Scholar
  19. 19.
    MathWorks. (2015, June) R2013a imregister. [Online]. http://www.mathworks.com/help/images/ref/imregister.html

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kosmas Dimitropoulos
    • 1
  • Panagiotis Barmpoutis
    • 1
  • Triantafyllia Koletsa
    • 2
  • Ioannis Kostopoulos
    • 2
  • Nikos Grammalidis
    • 1
    Email author
  1. 1.Centre for Research and Technology HellasInformation Technologies InstituteThessalonikiGreece
  2. 2.Medical SchoolAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations