Texture and object related image analysis in microscopic images

  • Klaus Kayser Charite - Berlin
  • Stephan Borkenfeld http://orcid.org/0000-0002-1476-8083
  • Amina Djenouni Pathology Institute, Batna (Algeria)
  • Gian Kayser Institute of Pathology, University, Freiburg, Germany



Tissue based diagnosis or surgical diagnostic pathology undergoes significant changes and focuses on image content analysis in our days. Herein we describe and discuss new approaches of content image analysis and compare their applications, benefits and constraints.


Any useful microscopic image contains information that can be evaluated and transferred into a tissue-based diagnosis. A correctly derived diagnosis depends upon the image information and the pathologist’s knowledge, i.e. his ability to recognize and transfer the image content information into clinical application. Thus, image information is related to external “disease†information and “pure†image content information. Application of external image information requires a separation of objects from the background, or segmentation procedures. “Pure†image information is solely pixel based. It can be analyzed using different approaches, such as entropy measure, construction of image primitives and their spatial distribution, or image similarity operations. Our approach uses entropy calculations dependent upon all possible gray value thresholds in combination with syntactic analysis of pixel based image primitives.


Virtual slides underwent the evaluation of regions of interest (ROI) as described previously. ROIs were interactively controlled and subject for application of developed image analysis procedures. The “classic method†of object recognition and syntactic structure analysis is applied too. Trials were performed on antie Her2_new stained, DAB visualized, and glycohistochemically stained, AP visualized slides. The images were measured at magnifications, which correspond to x20, and x40 objectives.


The algorithm displayed only weak changes of the evaluated gray level based structural entropy (GL-MST)-entropy in the selected ROIs in contrast to the whole image. In addition, significant differences could be obtained when the measures were associated to the clinical impact (diagnosis, fetal developing stage).
Conclusions: Images textures and pixel primitives can serve for evaluating “pure†image content information. They do not require segmentation and additional external information for measurement. Interpretation of the measures, i.e. external information can be implemented at a later stage. The described algorithm is probably applicable for image analysis of different fields such as histology, forests, traffic, or can serve for objective image quality evaluation.


Download data is not yet available.


1. Racoceanu, D. and P. Belhomme, Breakthrough technologies in digital pathology. Comput Med Imaging Graph. 42: p. 1.

2. Wright, A.I., H.I. Grabsch, and D.E. Treanor, RandomSpot: A web-based tool for systematic random sampling of virtual slides. J Pathol Inform. 6: p. 8.

3. Lundstrom, C., et al., Summary of 2(nd) Nordic symposium on digital pathology. J Pathol Inform. 6: p. 5.

4. Al-Janabi, S., et al., Whole slide images for primary diagnostics of urinary system pathology: a feasibility study. J Renal Inj Prev. 3(4): p. 91-6.

5. Fine, J.L., 21(st) century workflow: A proposal. J Pathol Inform. 5: p. 44.

6. Greenbaum, A., et al., Wide-field computational imaging of pathology slides using lens-free on-chip microscopy. Sci Transl Med. 6(267): p. 267ra175.

7. Kumar, A., et al., Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers. Proc Natl Acad Sci U S A. 111(51): p. 18249-54.

8. Amselgruber, W.M., et al., Expression of galactosyltransferase in prostatic tumors. Nutrition, 1995. 11(5 Suppl): p. 638-42.

9. Kobara, H., et al., Analysis of the amount of tissue sample necessary for mitotic count and Ki-67 index in gastrointestinal stromal tumor sampling. Oncol Rep. 33(1): p. 215-22.

10. Kayser , K., B. Molnar, and R.S. Weinstein, Virtual Microscopy Fundamentals - Applications - Perspectives of Electronic Tissue - based Diagnosis. 2006, Berlin: VSV Interdisciplinary Medical Publishing.

11. Kayser, K., et al., Digitized pathology: theory and experiences in automated tissue-based virtual diagnosis. Rom J Morphol Embryol, 2006. 47(1): p. 21-8.

12. Kayser, K. and H. Hoffgen, Pattern recognition in histopathology by orders of textures. Med Inform (Lond), 1984. 9(1): p. 55-9.

13. Kayser, K. and W. Schlegel, Pattern recognition in histo-pathology: basic considerations. Methods Inf Med, 1982. 21(1): p. 15-22.

14. Kayser, G., et al., Towards an automated morphological classification of histological images of common lung carcinomas. Elec J Pathol Histol, 2002. 8: p. 022-03.

15. von Goethe, J.W., Zur Farbenlehre. 1810, Tübingen: Cotta.

16. Kayser, K., et al., AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis. Folia Histochem Cytobiol, 2009. 47(3): p. 355-61.

17. Corredor, G., E. Romero, and M. Iregui, An adaptable navigation strategy for Virtual Microscopy from mobile platforms. J Biomed Inform.

18. Moles Lopez, X., et al., An automated blur detection method for histological whole slide imaging. PLoS One. 8(12): p. e82710.

19. Kayser, K., et al., Theory of sampling and its application in tissue based diagnosis. Diagn Pathol, 2009. 4: p. 6.

20. Gray, A., et al., Quantification of histochemical stains using whole slide imaging: development of a method and demonstration of its usefulness in laboratory quality control. J Clin Pathol. 68(3): p. 192-9.

21. Khalbuss, W.E., J. Cuda, and I.C. Cucoranu, Screening and dotting virtual slides: A new challenge for cytotechnologists. Cytojournal. 10: p. 22.

22. Keller, K.K., et al., Improving efficiency in stereology: a study applying the proportionator and the autodisector on virtual slides. J Microsc. 251(1): p. 68-76.

23. Garcia-Rojo, M., L. Goncalves, and B. Blobel, The COST Action IC0604 "Telepathology Network in Europe" (EURO-TELEPATH). Stud Health Technol Inform. 179: p. 3-12.

24. Oger, M., et al., Automated region of interest retrieval and classification using spectral analysis. Diagn Pathol, 2008. 3 Suppl 1: p. S17.

25. Li, X. and K. Plataniotis, A Complete Color Normalization Approach To Histo-pathology Images Using Color Cues Computed From Saturation-Weighted Statistics. IEEE Trans Biomed Eng.

26. Voss, K. and H. Süsse, Praktische Bildverarbeitung. 1991: München, Wien: Carl Hanser Verlag.

27. Kayser, K. and H.J. Gabius, The application of thermodynamic principles to histochemical and morphometric tissue research: principles and practical outline with focus on the glycosciences. Cell Tissue Res, 1999. 296(3): p. 443-55.

28. Kayser, K., et al., Glyco- and immunohistochemical refinement of the differential diagnosis between mesothelioma and metastatic carcinoma and survival analysis of patients. J Pathol, 2001. 193(2): p. 175-80.

29. Kayser, K., et al., Application of computer-assisted morphometry to the analysis of prenatal development of human lung. Anat Histol Embryol, 1997. 26(2): p. 135-9.

30. Kayser, K., et al., Carcinoid tumors of the lung: immuno- and ligandohistochemistry, analysis of integrated optical density, syntactic structure analysis, clinical data, and prognosis of patients treated surgically. J Surg Oncol, 1996. 63(2): p. 99-106.

31. Kayser, K., et al., Association of prognosis in surgically treated lung cancer patients with cytometric, histometric and ligand histochemical properties: with an emphasis on structural entropy. Anal Quant Cytol Histol, 1998. 20(4): p. 313-20.

32. Virchow, R., Cellular Pathologie. Virchows. Arch. Path. Anat., 1855. 8: p. 3-39.

33. Remak, R., Ein Beitrag zur Entwicklungsgeschichte der krebshaften Geschwuelste. Dtsch. Klin., 1854. 6: p. 170-175.

34. Park, S., et al., The history of pathology informatics: A global perspective. J Pathol Inform. 4: p. 7.

35. Kayser, K., et al., New developments in digital pathology: from telepathology to virtual pathology laboratory. Stud Health Technol Inform, 2004. 105: p. 61-9.

36. Szoke, T., et al., Prognostic significance of endogenous adhesion/growth-regulatory lectins in lung cancer. Oncology, 2005. 69(2): p. 167-74.

37. Szoke, T., et al., Prognostic significance of microvascularization in cases of operated lung cancer. Eur J Cardiothorac Surg, 2005. 27(6): p. 1106-11.

38. Zink, S., et al., Survival, disease-free interval, and associated tumor features in patients with colon/rectal carcinomas and their resected intra-pulmonary metastases. Eur J Cardiothorac Surg, 2001. 19(6): p. 908-13.

39. Werle, B., et al., Cathepsin B in infiltrated lymph nodes is of prognostic significance for patients with nonsmall cell lung carcinoma. Cancer, 2000. 89(11): p. 2282-91.

40. Kayser, K., et al., Pulmonary metastases of breast carcinomas: ligandohistochemical, nuclear, and structural analysis of primary and metastatic tumors with emphasis on period of occurrence of metastases and survival. J Surg Oncol, 1998. 69(3): p. 137-46.

41. Schmitt, F., HER2+ breast cancer: how to evaluate? Adv Ther, 2009. 26 Suppl 1: p. S1-8.

42. Bilous, M., et al., Predicting the HER2 status of breast cancer from basic histopathology data: an analysis of 1500 breast cancers as part of the HER2000 International Study. Breast, 2003. 12(2): p. 92-8.

43. Aitken, S.J., et al., Quantitative analysis of changes in ER, PR and HER2 expression in primary breast cancer and paired nodal metastases. Ann Oncol. 21(6): p. 1254-61.

44. Sanfeliu, A., K.S. Fu, and J.M.S. Prewitt. An application ofa distance measure between graphs to the analysis of muscle tissue patterns. in Workshop on Structural and Syntactic Pattern recognition. 1981. Saratoga Springs, New Yorck.

45. Prewitt, J.M.S. and S.C. Wu. An application of pattern recognition to epithelial tissues. in Computer Applications in Medical Care. 1978: IEEE Computer Society.

46. Lu, S.Y. and K.S. Fu, A syntactic approach to texture analysis. Computer Garphics Image Processing, 1978. 7: p. 303 - 330.
47. Lu, S.Y., A tree-to-tree distance and its application to cluster analysis. IEEE Trans Pattern Analysis and Maschine Intelligence, 1979. PAMI-1: p. 219-234.

48. Helps, S.C., et al., Automatic nonsubjective estimation of antigen content visualized by immunohistochemistry using color deconvolution. Appl Immunohistochem Mol Morphol. 20(1): p. 82-90.

49. Kayser, K., et al., Texture- and object-related automated information analysis in histological still images of various organs. Anal Quant Cytol Histol, 2008. 30(6): p. 323-35.

50. Walkowski, S. and J. Szymas, Histopathologic patterns of nervous system tumors based on computer vision methods and whole slide imaging (WSI). Anal Cell Pathol (Amst). 35(2): p. 117-22.

51. Wang, F., et al., A data model and database for high-resolution pathology analytical image informatics. J Pathol Inform. 2: p. 32.
How to Cite
KAYSER, Klaus et al. Texture and object related image analysis in microscopic images. Diagnostic Pathology, [S.l.], may 2015. ISSN 2364-4893. Available at: <http://www.diagnosticpathology.eu/content/index.php/dpath/article/view/14>. Date accessed: 15 apr. 2024. doi: https://doi.org/10.17629/www.diagnosticpathology.eu-2015-1:14.


Tissue-based diagnosis; content image analysis; MST entropy; Immunohistochemistry; Glycohistochemistry; Segmentation