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Colour Model Analysis for Histopathology Image Processing

Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 6)

Abstract

This chapter presents a comparative study among different colour models (RGB, HSI, CMYK, CIEL*a*b*, and HSD) applied to very large microscopic image analysis. Such analysis of different colour models is needed in order to carry out a successful detection and therefore a classification of different regions of interest (ROIs) within the image. This, in turn, allows both distinguishing possible ROIs and retrieving their proper colour for further ROI analysis. This analysis is not commonly done in many biomedical applications that deal with colour images. Other important aspect is the computational cost of the different processing algorithms according to the colour model. This work takes these aspects into consideration to choose the best colour model tailored to the microscopic stain and tissue type under consideration and to obtain a successful processing of the histological image.

Keywords

Colour Model Optical Density Histological Image Whole Slide Imaging Bismarck Brown 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgements

This work has been carried out with the support of the research projects DPI2008-06071 of the Spanish Research Ministry, PI-2010/040 of the FISCAM and PAI08-0283-9663 of JCCM. We extend our gratitude to the Department of Pathology at Hospital General Universitario de Ciudad Real for providing the tissue samples.

References

  1. 1.
    Avwioro G (2011) Histochemical uses of haematoxylin—a review. J Pharm Clin Sci 1:24–34 Google Scholar
  2. 2.
    Begelman G, Gur E, Rivlin E, Rudzsky M, Zalevsky Z (2004) Cell nuclei segmentation using fuzzy logic engine. In: Proceedings of the IEEE international conference on image processing, pp 2937–2940 Google Scholar
  3. 3.
    Belkacem-Boussaid K, Samsi S, Lozanski G, Gurcan MN (2011) Automatic detection of follicular regions in H&E images using iterative shape index. Comput Med Imaging Graph 35(7–8):592–602 CrossRefGoogle Scholar
  4. 4.
    Berk T, Kaufman A, Brownston L (1982) A human factors study of color notation systems for computer graphics. Commun ACM 25(8):547–550 CrossRefGoogle Scholar
  5. 5.
    Carson F, Hladik C (2009) Histotechnology: a self-instructional text, 3rd edn. American Society for Clinical Pathology, Hong Kong Google Scholar
  6. 6.
    Cataldo SD, Ficarra E, Acquaviva A, Macii E (2010) Achieving the way for automated segmentation of nuclei in cancer tissue images through morphology-based approach: a quantitative evaluation. Comput Med Imaging Graph 34(6):453–461 CrossRefGoogle Scholar
  7. 7.
    Cataldo SD, Ficarra E, Acquaviva A, Macii E (2010) Automated segmentation of tissue images for computerized IHC analysis. Comput Methods Programs Biomed 100(1):1–15 CrossRefGoogle Scholar
  8. 8.
    Daniel C, Rojo MG, Klossa J, Della Mea V, Booker D, Beckwith BA, Schrader T (2011) Standardizing the use of whole slide images in digital pathology. Comput Med Imaging Graph 35(7–8):496–505 CrossRefGoogle Scholar
  9. 9.
    Decaestecker C, Lopez XM, D’Haene N, Roland I, Guendouz S, Duponchelle C, Berton A, Debeir O, Salmon I (2009) Requirements for the valid quantification of immunostains on tissue microarray materials using image analysis. Proteomics 9(19):4478–4494 CrossRefGoogle Scholar
  10. 10.
    DiFranco MD, O’Hurley G, Kay EW, Watson, Cunningham P (2011) Ensemble based system for whole-slide prostate cancer probability mapping using color texture features. Comput Med Imaging Graph 35(7–8):629–645 CrossRefGoogle Scholar
  11. 11.
    Douglas S, Kirkpatrick A (1999) Model and representation: the effect of visual feedback on human performance in a color picker interface. ACM Trans Graph 18(2):96–127 CrossRefGoogle Scholar
  12. 12.
    Doyle S, Feldman M, Tomaszewski J, Madabhushi A (2012) A boosted bayesian multi-resolution classifier for prostate cancer detection from digitized needle biopsies. In: IEEE transactions on biomedical engineering, pp 1–14 Google Scholar
  13. 13.
    Gao M, Bridgman P (2003) Computer aided prostate cancer diagnosis using image enhancement and JPEG2000. In: Proceedings of the SPIE international conference on applications of digital image processing, vol 5203, pp 323–334 Google Scholar
  14. 14.
    García M, Bueno G, Peces C, González J, Carbajo M (2006) Critical comparison of 31 commercially available digital slide systems in pathology. Int J Surg Pathol 14(4):285–305 CrossRefGoogle Scholar
  15. 15.
    Hafiane A, Bunyak F, Palaniappan K (2008) Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection. In: Advanced concepts for intelligent vision systems, vol 5259, pp 903–914 CrossRefGoogle Scholar
  16. 16.
    Hafiane A, Bunyak F, Palaniappan K (2008) Level set-based histology image segmentation with region-based comparison. In: Proceedings of the third international workshop on microscopic image analysis with applications in biology Google Scholar
  17. 17.
    Hu MP, Ding XY (2004) Automated cell nucleus segmentation using improved snake. In: Proceedings of the IEEE international conference on image processing, vol 4, pp 2737–2740 Google Scholar
  18. 18.
    Huang CH, Veillard A, Roux L, Loménie N, Racoceanu D (2011) Time-efficient sparse analysis of histopathological whole slide images. Comput Med Imaging Graph 35:579–591 CrossRefGoogle Scholar
  19. 19.
    Huang PW, Lee CH (2009) Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans Med Imaging 28(7):1037–1050 CrossRefGoogle Scholar
  20. 20.
    Hunt R (2004) The reproduction of colour, 6th edn. Wiley-IS&T series in imaging science and technology CrossRefGoogle Scholar
  21. 21.
    Kiernan J (2008) Histological and histochemical methods: theory and practice, 4th edn. Scion Publishing Ltd, Bloxham Google Scholar
  22. 22.
    der Laak JV, Pahlplatz M, Hanselaar A, de Wilde P (2000) Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy. Cytometry 39(4):275–284 CrossRefGoogle Scholar
  23. 23.
    Lezoray O, Gurcan M, Can A, Olivo-Marin JC (2011) Whole slide microscopic image processing—special issue editorial. Comput Med Imaging Graph 35(7–8):493–495 CrossRefGoogle Scholar
  24. 24.
    Llewellyn B (2009) Nuclear staining with alum-hematoxylin. Biotech Histochem 84:159–177 CrossRefGoogle Scholar
  25. 25.
    Lopez XM, Debeir O, Maris C, Roland I, Salmon I, Decaestecker C (2010) KI-67 hot-spots detection on glioblastoma tissue sections, pp 149–152 Google Scholar
  26. 26.
    Madabhushi A (2009) Digital pathology image analysis: opportunities and challenges. Imag Med 1(1):7–10 CrossRefGoogle Scholar
  27. 27.
    Madabhushi A, Agner S, Basavanhally A, Doyle S, Lee G (2011) Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data. Comput Med Imaging Graph 35(7–8):506–514 CrossRefGoogle Scholar
  28. 28.
    Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: Proceedings of the 2008 IEEE international symposium on biomedical imaging: from nano to macro, pp 284–287 CrossRefGoogle Scholar
  29. 29.
    Peng Y, Jiang Y, Eisengart L, Healy M, Straus FH, Yang X (2011) Computer-aided identification of prostatic adenocarcinoma: segmentation of glandular structures. J Pathol Inform 2(33):1–10 Google Scholar
  30. 30.
    Prasad K, Tiwari A, Ilanthodi S, Prabhu G, Pai M (2011) Automation of immunohistochemical evaluation in breast cancer using image analysis. World J Clin Oncol 2(4):187–194 CrossRefGoogle Scholar
  31. 31.
    van Putten M, de Winter C, van Roon-Mom W, van Ommen G, Hoen P, Aartsma-Rus A (2010) A 3 months mild functional test regime does not affect disease parameters in young mdx mice. Neuromuscul Disord 20:273–283 CrossRefGoogle Scholar
  32. 32.
    Roullier V, Lezoray O, Ta VT, Elmoataz A (2011) Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization. Comput Med Imaging Graph 35(7–8):603–615 CrossRefGoogle Scholar
  33. 33.
    Ruifrok A, Johnston D (2001) Quantification of histological staining by color deconvolution. Anal Quant Cytol Histol 23:291–299 Google Scholar
  34. 34.
    Ruifrok A, Katz R, Johnston D (2004) Comparison of quantification of histochemical staining by hue-saturation-intensity (HSI) transformation and color deconvolution. Appl Immunohistochem Mol Morphol 11(1):85–91 CrossRefGoogle Scholar
  35. 35.
    Schwarz M, Cowan W, Beatty J (1987) An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models. ACM Trans Graph 6(2):123–158 CrossRefGoogle Scholar
  36. 36.
    Selvin E, Najjar S, Cornish T, Halushka M (2010) A comprehensive histopathological evaluation of vascular medial fibrosis: insights into the pathophysiology of arterial stiffening. Atherosclerosis 208(1):69–74 CrossRefGoogle Scholar
  37. 37.
    Sertel O, Lozanski G, Shana’ah A, Gurcan MN (2010) Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation. IEEE Trans Biomed Eng 57(10):2613–2616 CrossRefGoogle Scholar
  38. 38.
    Smith A (1982) Color gamut transform pairs. Comput Graph 12(3):12–19 Google Scholar
  39. 39.
    Svaetichin G (1956) Spectral response curves from single cones. Actaphysiol 134:17–46 Google Scholar
  40. 40.
    Tabesh A, Kumar V, Verbel D, Kotsianti A, Teverovskiy M, Saidi O (2002) Automated prostate cancer diagnosis and Gleason grading of tissue microarrays. In: Proceedings of the SPIE medical imaging conference, vol 5747, pp 58–70 Google Scholar
  41. 41.
    Taneja T, Sharma S (2004) Markers of small cell lung cancer. World J Surg Oncol 2:10 CrossRefGoogle Scholar
  42. 42.
    Tuominen V, Ruotoistenmaki S, Viitanen A, Jumppanen M, Isola J (2010) ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res 12(4):R56 CrossRefGoogle Scholar
  43. 43.
    Valenzuela O, Rojas I, Rojas F, Fernandez L (2005) Automatic classification of prostate cancer using pseudo-Gaussian radial basis function neural network. In: Proceedings of the European symposium on artificial neural networks, pp 145–150 Google Scholar
  44. 44.
    Vidal J, Bueno G, Galeotti J, García-Rojo M, Relea F, Déniz O (2011) A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering. J Pathol Inform 2(5):1–11 Google Scholar
  45. 45.
    Wyszecki G, Stiles W (2000) Color science: concepts and methods, quantitative data and formulae, 2nd edn. Wiley, New York Google Scholar
  46. 46.
    Xu J, Madabhushi A, Janowczyk A, Chandran S (2010) A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: applications to histopathology image segmentation. Proceedings of the SPIE medical imaging conference, vol 7623 Google Scholar
  47. 47.
    Yang L, Meer P, Foran D (2005) Unsupervised segmentation based on robust estimation and colour active contour models. IEEE Trans Inf Technol Biomed 9(3):475–486 CrossRefGoogle Scholar
  48. 48.
    Yongming L, Dongming L, Xiqun L, Jianming L (2004) Interactive colour image segmentation by region growing combines with image enhancement based on Bezier model. In: Proceedings of the third international conference on image and graphics, vol 100, pp 96–99 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.VISILAB, E.T.S.I.IUniversidad de Castilla-La ManchaCiudad RealSpain
  2. 2.Dpt. Anatomía PatológicaHospital General Universitario de Ciudad RealCiudad RealSpain

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