Medical and Biological Engineering and Computing

, Volume 36, Issue 6, pp 679–685 | Cite as

Roughness feature of metaphase chromosome spreads and nuclei for automated cell proliferation analysis

  • G. Corkidi
  • L. Vega
  • J. Márquez
  • E. Rojas
  • P. Ostrosky-Wegman


As a step towards automation of mitotic index estimation for cell proliferation studies, a roughness feature of surface-intensity images is introduced: the mean depthwidth ratio of extrema (MDWRE). This feature allows identification of variable-shaped metaphases and interphase nuclei in the presence of many artefacts (one metaphase per hundreds of nuclei and thousands of artefacts). The texture of the cytological objects (seen as rough surfaces) is quantified by scanning, in one dimension, the lines contained in a closed contour. MDWRE proves to be suitable for image magnifications by a factor of as low as ten, making faster scanning of slides possible. The use of this feature gives +14%, +65%, +133% and +133% better performance figures than classical textural features derived from co-occurrence matrices, such as contrast, energy, entropy and angular second moment, respectively, and +51% better than the relative extrema density (RED). The MDWRE per object and the shape of the histogram of the depth-width ratio of grey-level roughs have been shown to be very useful as textural features for the classification of metaphase images.


Mitotic index Image texture classification Roughness Cell proliferation Quantitative microscopy 


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  1. Barile, F. A. (Ed.), (1994): ‘In vitro cytotoxicology. Mechanisms and method’, (CRC Press, USA) pp. 1–222Google Scholar
  2. Castleman, K. R. (1992): ‘The PSI automatic metaphase finder’,J. Radiat. Res.,33, (Suppl.), pp. 124–128Google Scholar
  3. Corkidi, G., andMarquez, J. (1992): ‘Microestación de procesamiento digital de imágenes para aplicaciones biomédicas: instrumentación y metodologías,Instrum. Dev.,3, (2), pp. 62–70Google Scholar
  4. Garza-Jinich, M., Rodriguez, C., Corkidi, G., Montero, R., Rojas, E., andOstrosky-Wegman, P. (1992): ‘A microcomputer-based supervised system for automatic scoring of mitotic index in citotoxicity studies’,in Archibald, C., andPetriu, E. (Eds.): ‘Advances in machine vision, vol. 32’, (World Scientific Press, series in Computer Science) pp. 301–313Google Scholar
  5. Green, D. K., andNeurath, P. W. (1974): ‘The design, operation and evaluation of a high speed automatic metaphase finder’,J. Histochem. Cytochem.,22, (7), pp. 531–535Google Scholar
  6. Haralick, R. M. (1979): ‘Statistical and structural approaches to texture’,Proc. IEEE,67, pp. 786–804CrossRefGoogle Scholar
  7. Haralick, R. M., andShapiro, L. G. (1992): ‘Computer and robot vision I’ (Addison-Wesley) pp. 471–473Google Scholar
  8. Hemminger, B. M., Johnston, R. E., Rolland, J. P., andMuller, K. E. (1994): ‘Perceptual linearization of video display monitors for medical image presentation’,SPIE Proc.,2164, pp. 222Google Scholar
  9. Jain, A. K. (1989): ‘Fundamentals of digital image processing’ (Prentice Hall), pp. 394–400Google Scholar
  10. Johnson, E. T., andGoforth, L. J. (1974): ‘Metaphase spread detection and focus using closed circuit television’,J. Histochem. Cytochem.,22, (7), 536–545Google Scholar
  11. Otsu, N. (1979): ‘A threshold selection method from gray-level histograms’,IEEE Trans.,SMC-9, pp. 62–66Google Scholar
  12. Perry, P., andWolff, S. (1974): ‘New giemsa method fordifferential staining of sister-chromatids’,Nature,251, pp. 156–158CrossRefGoogle Scholar
  13. Piper, J., Poggensee, M., Hill, W., Jensen, R., Jr., L. Poole, I., Stark, M., andSudar, D. (1994): ‘Automatic fluorescence metaphase finder speeds translocation scoring in FISH painted chromosomes’,Cytometry 1,16 (1), pp. 7–16CrossRefGoogle Scholar
  14. Poyton, C. H. (1996): ‘A technical introduction to digital video’, (John Wiley & Sons, New York), pp. 91–114Google Scholar
  15. Rojas, E., Montero, R., Herrera, L. A., Sordo, M., Gonsebatt, M. E., Rodriguez, R., andOstrosky-Wegman, P. (1992): ‘Are mitotic index and lymphocyte proliferation kinetic reproducible endpoints in genetic toxicology testing?’,Mutation Res.,282, pp. 283–286CrossRefGoogle Scholar
  16. Rojas, E., Herrera, L. A., Sordo, M., Gonsebatt, M. E., Montero, R., Rodriguez, R., andOstrosky-Wegman, P. (1993): ‘Mitotic index and lymphocyte proliferation kinetics for identification of antineoplastic activity’,Anti-cancer Drugs,4, (6), pp. 637–640CrossRefGoogle Scholar
  17. Rosenfeld, A., andTroy, E. (1970): ‘Visual texture analysis’. Technical Report, University of Maryland, College Park, pp. 70–116Google Scholar
  18. Serra, J. (1989): ‘Image analysis and mathematical morphology’ (Academic Press), pp. 43–49Google Scholar
  19. Stoyan, D., Kendall, W. S., andMecke, J. (1987): ‘Stochastic geometry and its applications’ (Wiley), pp. 42–56Google Scholar
  20. Vrolijk, J., Sloos, W. C., Darroudi, F., Natarajan, A. T., andTanke, H. J (1994): ‘A system for fluorescene metaphase finding and scoring of chromosomal translocations visualized byin situ hybridization’,Int. J. Radiat. Biol.,66, (3), pp. 287–95Google Scholar
  21. Weber, J., Scheid, W., andTraut, H. (1992): ‘Time-saving in biological dosimetry by using the automatic metaphase finder Metafer2’,Mutat. Res.,272, (1), pp. 31–34Google Scholar
  22. Weibel, E. R. (1979a): ‘Stereological methods, vol. I’ (Academic Press), pp. 2–13Google Scholar
  23. Weibel, E. R. (1979b): ‘Stereological methods, vol. II’ (Academic Press), pp. 47–54Google Scholar

Copyright information

© IFMBE 1998

Authors and Affiliations

  • G. Corkidi
    • 1
  • L. Vega
    • 1
  • J. Márquez
    • 3
  • E. Rojas
    • 2
  • P. Ostrosky-Wegman
    • 2
  1. 1.Image Processing Laboratory, Centro de InstrumentosUNAMMéxico
  2. 2.Genetic Toxicology Laboratory, Instituto de Investigaciones BiomédicasUNAM
  3. 3.Image DepartmentEcole Nationale Supérieure des TélécommunicationsParisFrance

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