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Digital Image Analysis and Virtual Microscopy in Pathology

  • Pranab Dey
Chapter

Abstract

Digital image analysis (DIA) gives objective and consistent information of the images and helps in the diagnosis, grading, classification and various prognostic information of diseases. This chapter elucidates the principle, steps and application of DIA. The steps of DIA include image digitalization, image detection, image segmentation, image editing and feature extraction. The problems of DIA such as auto-segmentation, decision to take on individual patient, getting three-dimensional imaging data from two-dimensional data, etc. are also discussed. Presently the entire slide is available in the computer as “whole slide imaging” with the help of whole slide scanning. In this whole slide imaging, a complete digital slide is generated, and the observer can examine any part of the slide by increasing or decreasing the magnification. The chapter covers the advantages and disadvantages of virtual slide and web-based teaching.

Keywords

Digital image analysis Image digitalization Image detection Image segmentation Image editing Feature extraction Auto-segmentation Telepathology Markovian texture Grey level co-occurrence of matrix Pattern recognition Virtual slide Web-based teaching Whole slide imaging 

References

  1. 1.
    Laurinavicius A, Laurinaviciene A, Dasevicius D, Elie N, Plancoulaine B, Bor C, Herlin P. Digital image analysis in pathology: benefits and obligation. Anal Cell Pathol (Amst). 2012;35(2):75–8.CrossRefGoogle Scholar
  2. 2.
    Qi X, Wang D, Rodero I, Diaz-Montes J, Gensure RH, Xing F, Zhong H, Goodell L, Parashar M, Foran DJ, Yang L. Content-based histopathology image retrieval using CometCloud. BMC Bioinf. 2014;15:287.  https://doi.org/10.1186/1471-2105-15-287.CrossRefGoogle Scholar
  3. 3.
    Farahani N, Pantanowitz L. Overview of telepathology. Surg Pathol Clin. 2015;8(2):223–31.CrossRefPubMedGoogle Scholar
  4. 4.
    Chan SWK, Leung KS, Wong WSF. An expert system for the detection of cervical cancer cells using knowledge-based image analyzer. Artif Intell Med. 1996;8(1):67–90.CrossRefPubMedGoogle Scholar
  5. 5.
    De Solórzano CO, Costes S, Callahan DE, Parvin B, Barcellos-Hoff MH. Applications of quantitative digital image analysis to breast cancer research. Microsc Res Tech. 2002;59(2):119–27.CrossRefGoogle Scholar
  6. 6.
    Vega-Alvarado L, Márquez J, Corkidi G. Interchromosome texture as a feature for automatic identification of metaphase spreads. Med Biol Eng Comput. 2002;40:479–84.CrossRefPubMedGoogle Scholar
  7. 7.
    Tabesh A, Teverovskiy M, Pang HY, Kumar VP, Verbel D, Kotsianti A, Saidi O. Multifeature prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans Med Imaging. 2007;26:1366–78.CrossRefPubMedGoogle Scholar
  8. 8.
    Weyn B, Wouver G, Daele A, et al. Automated breast tumor diagnosis and grading based on wavelet chromatin texture description. Cytometry. 1998;33:32–40.CrossRefPubMedGoogle Scholar
  9. 9.
    Mulrane L, Rexhepaj E, Penney S, Callanan JJ, Gallagher WM. Automated image analysis in histopathology: a valuable tool in medical diagnostics. Expert Rev Mol Diagn. 2008;8(6):707–25.CrossRefPubMedGoogle Scholar
  10. 10.
    Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: challenges and opportunities. Med Image Anal. 2016;33:170–5.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    He L, Long LR, Antani S, Thoma GR. Histology image analysis for carcinoma detection and grading. Comput Methods Prog Biomed. 2012;107(3):538–56.CrossRefGoogle Scholar
  12. 12.
    Losa GA, Castelli C. Nuclear patterns of human breast cancer cells during apoptosis: characterisation by fractal dimension and co-occurrence matrix statistics. Cell Tissue Res. 2005;322:257–67.CrossRefPubMedGoogle Scholar
  13. 13.
    Lezoray O, Elmoataz A, Cardot H, Gougeon G, Lecluse M, Elie H, Revenu M. Segmentation of cytological images using color and mathematical morphology. Eur Conf Stereol. 1999;18(1):1–14.Google Scholar
  14. 14.
    Jiang K, Liao QM, Xiong Y. A novel white blood cell segmentation scheme based on feature space clustering. Soft Comput. 2006;10:12–9.CrossRefGoogle Scholar
  15. 15.
    Madabhushi A, Doyle S, Lee G, Basavanhally A, Monaco J, Masters S, Tomaszewski J, Feldman M. Integrated diagnostics: a conceptual framework with examples. Clin Chem Lab Med. 2010;48:989–98.CrossRefPubMedGoogle Scholar
  16. 16.
    Turbin DA, Leung S, Cheang MC, Kennecke HA, Montgomery KD, McKinney S, Treaba DO, Boyd N, Goldstein LC, Badve S, Gown AM, van de Rijn M, Nielsen TO, Gilks CB, Huntsman DG. Automated quantitative analysis of estrogen receptor expression in breast carcinoma does not differ from expert pathologist scoring: a tissue microarray study of 3,484 cases. Breast Cancer Res Treat. 2008;110(3):417–26.CrossRefPubMedGoogle Scholar
  17. 17.
    López-Velázquez G, Márquez J, Ubaldo E, Corkidi G, Echeverría O, Vázquez Nin GH. Three-dimensional analysis of the arrangement of compact chromatin in the nucleus of G0 rat lymphocytes. Histochem Cell Biol. 1996;105:153–61.CrossRefPubMedGoogle Scholar
  18. 18.
    Paulsen FP, Eichhorn M, Brauer L. Virtual microscopy – the future of teaching histology in the medical curriculum? Ann Anat. 2010;192:378–82.CrossRefPubMedGoogle Scholar
  19. 19.
    Roerdink J, Meijster A. The watershed transform: definitions, algorithms and parallelization strategies. Fundam Inform. 2000;41:187–228.Google Scholar
  20. 20.
    Ordi O, Bombí JA, Martínez A, Ramírez J, Alòs L, Saco A, Ribalta T, Fernández PL, Campo E, Ordi J. Virtual microscopy in the undergraduate teaching of pathology. J Pathol Inform. 2015;6:1.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Pranab Dey
    • 1
  1. 1.Education and Research (PGIMER)Post Graduate Institute of Medical Education and Research (PGIMER)ChandigarhIndia

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