Segmentation of Nuclei from Breast Histopathology Images Using PSO-based Otsu’s Multilevel Thresholding

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)

Abstract

Automated histopathology image analysis involves segmentation of nuclei from the surrounding tissue structures to develop a computer-aided diagnosis (CAD) system. In this paper, we propose the use of particle swarm optimization (PSO)-based Otsu’s multilevel thresholding technique to automatically segment the nuclei from hematoxylin and eosin (H&E)-stained breast histopathology images. Otsu’s threshold selection problem is modeled as an optimization problem by designating the discriminant criterion as the objective or fitness function that has to be maximized. PSO is used to compute the optimal threshold value that maximizes the objective function. This paper studies the effectiveness of the proposed technique to segment nuclei from breast histopathology images.

Keywords

Automated histopathology Computer-aided diagnosis Particle swarm optimization Hepatocellular carcinoma Otsu’s multilevel thresholding 

References

  1. 1.
    P.-W. Huang, Y.-H. Lai, Effective segmentation and classification for HCC biopsy images. Pattern Recogn. 43(4), 1550–1563 (2010)CrossRefGoogle Scholar
  2. 2.
    Y. Al-Kofahi, W. Lassoued, W. Lee, B. Roysam, Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans. Biomed. Eng. 57(4), 841–852 (2010)CrossRefGoogle Scholar
  3. 3.
    M.M. Dundar, S. Badve, G. Bilgin, V. Raykar, R. Jain, O. Sertel, M.N. Gurcan, Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans. Biomed. Eng. 58(7), 1977–1984 (2011)CrossRefGoogle Scholar
  4. 4.
    C. Lu, M. Mahmood, N. Jha, M. Mandal, A robust automatic nuclei segmentation technique for quantitative histopathological image analysis. Anal. Quant. Cytol. Histol. 34(6), 296–308 (2012)Google Scholar
  5. 5.
    M. Veta, P.J. van Diest, R. Kornegoor, A. Huisman, M.A. Viergever, J.P.W. Pluim, Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PLoS ONE 8(7), e70221 (2013)Google Scholar
  6. 6.
    P. Ghamisi, M.S. Couceiro, J.A. Benediktsson, M.F. Nuno, N.M. Ferreira, An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. 39(16), 12407–12417 (2012)CrossRefGoogle Scholar
  7. 7.
    R.V. Kulkarni, G.K. Venayagamoorthy, Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Trans. Syst., Man, Cybern. 40(6), 663–675 (2010)CrossRefGoogle Scholar
  8. 8.
    E.D. Gelasca, J. Byun, B. Obara, B.S. Manjunath, Evaluation and benchmark for biological image segmentation, in IEEE International Conference on Image Processing (2008)Google Scholar
  9. 9.
    N. Otsu, A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, College of Engineering GuindyAnna UniversityChennaiIndia

Personalised recommendations