Advertisement

A Geometric Model for Image Analysis in Cytology

  • C. Ortiz de Solorzano
  • R. Malladi
  • S. J. Lockett
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

In this chapter, we propose a unified image analysis scheme for 3D computer assisted-cytology. The goal is to accurately extract and classify the shapes of nuclei and cells from confocal microscopy images. We make use of a geometry-driven scheme for preprocessing and analyzing confocal microscopy images. Namely, we build a chain of methods that includes an edge-preserving image smoothing mechanism, an automatic segmentation method, a geometry-driven scheme to regularize the shapes and improve edge fidelity, and an interactive method to split shape clusters and reclassify them. Finally we apply our scheme to segmenting nuclei using nuclear membrane and whole cells using cell-surface related proteins.

Keywords

Original Image Active Contour A6b1 Integrin Shape Recovery Confocal Microscope Image 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adalsteinsson D. and Sethian J.A.: A fast level set method for propagating interfaces. J. Comp. Phys. bf 118(2) (1995) 269–277MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Alvarez L., Guichard F., Lions P. L., and Morel J. M.: Axioms and fundamental equations of image processing. Arch. Rational Mechanics 123 (1993)Google Scholar
  3. 3.
    Ancin H., Roysam B., Dufresne T.E., Chesnut M.M., Ridder G.M., Szarowski D.H., Turner J.N.: Advances in Automated 3-D Image Analysis of Cell Populations Imaged by Confocal Microscopy. Cytometry 25 (1996) 221–234CrossRefGoogle Scholar
  4. 4.
    Ballard DH.: Generalizing the Hough Transform to detect arbitrary shapes. Pattern Recogn. 13 (1981) 111–122.zbMATHCrossRefGoogle Scholar
  5. 5.
    Balzer P., Furber A., Cavaro-Menard C., Croue A., Tadei A., Geslin P., Jallet P., Le Jeune JJ.: Simultaneous and correlated detection of endocardial and epicardial borders on short-axis MR images for the measurement of left ventricular mass. Radiographics 18 (1998) 1009–1018.Google Scholar
  6. 6.
    Bart M., Haar Romeny (Ed.): Geometry-driven diffusion in computer vision. Kluwer Academic Press, 1994Google Scholar
  7. 7.
    Bosman F.T.: Integrins: cell adhesives and modulators of cell function. Histochem. J. 25 (1993) 469–477CrossRefGoogle Scholar
  8. 8.
    Caselles V., Catte F., Coll T., Dibos F.: A geometric model for active contours. Numerische Mathematik 66 (1993) 1–31MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Caselles V., Kimmel R., Sapiro G.: Geodesic active contours. in Proc. ICCV’95, Cambridge, MA 1995Google Scholar
  10. 10.
    Cohen LD., Cohen I.: Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE T. Pattern Anal. 15 (1993) 1131–1146.CrossRefGoogle Scholar
  11. 11.
    Czader M., Liljeborg A., Auer G., Porwit A.: Confocal 3-Dimensional DNA Image Cytometry in Thick Tissue Sections. Cytometry 25 (1996) 246–253CrossRefGoogle Scholar
  12. 12.
    Dastidar P., Heinonen T., Numminen J., Rautiainen M., Laasonen E.: Semiautomatic segmentation of computed tomographic images in volumetric estimation of nasal airway. Eur. Arch. Oto-rhino-l. 256 (1999) 192–198.CrossRefGoogle Scholar
  13. 13.
    Dean P., Mascio L., Ow D., Sudar D., Mullikin J.: Proposed standard for image cytometry data files. Cytometry 11 (1990) 561–569CrossRefGoogle Scholar
  14. 14.
    Grayson M.: The heat equation shrinks embedded plane curves to round points. J. Differential Geometry 26 (1987) 285–314MathSciNetzbMATHGoogle Scholar
  15. 15.
    Heppner G. H.: Cell-to-cell interaction in regulating diversity of neoplasms. Seminars in Cancer Biology 2 (1991) 97–103Google Scholar
  16. 16.
    Irinopoulou T., Vassy J., Beil M., Nicolopoulou P., Encaoua D., Rigaut J.P.: Three-Dimensional DNA Image Cytometry by Confocal Scanning Laser Microscopy in Thick Tissue Blocks of Prostatic Lesions. Cytometry 27 (1997) 99–105CrossRefGoogle Scholar
  17. 17.
    Kikinis R., Guttman CRG., Metcalf MS., Wells WM., Gil J., Ettinger MD., Howard L., Weiner MD., Jolesz FA.: Quantitative follow-up of patients with multiple sclerosis using MRI: Technical aspects. J. Magn. Reson. Imaging 9 (1999) 519–530CrossRefGoogle Scholar
  18. 18.
    Koukoulis G.K., Virtanen I., Korhonen M., Laitinen L. Quarana V., Gould V.E.: Immunohistochemical localization of integrins in the normal, hyperplastic and neoplastic breast. Am. J. Pathol. bf 139 (1991) 787–799Google Scholar
  19. 19.
    Lelievre S., Weaver V.M., Bissell M.J.: Extracellular matrix signaling from the cellular membrane skeleton to the nuclear skeleton: A model of gene regulation. Recent Progress in Hormone Research 51 (1996) 417–432Google Scholar
  20. 20.
    Lockett S.J., Sudar D., Thompson C.T., Pinkel D., Gray J.W.: Efficient, interactive, three-dimensional segmentation of cell nuclei in thick tissue sections. Cytometry 31 (1998) 275–286CrossRefGoogle Scholar
  21. 21.
    Malladi R., Sethian J.A., Vemuri B.C.: A topology-independent shape modeling scheme. in SPIE: Geometric Methods in Computer Vision II, Vol. 2031 (1993) 246–258Google Scholar
  22. 22.
    Malladi R., Sethian J.A., Vemuri B.C.: Shape modeling with front propagation: A level set approach. IEEE Trans. on PAMI 17 (1995) 158–175CrossRefGoogle Scholar
  23. 23.
    Malladi R., Sethian J.A.: Image processing: Flows under Min/Max curvature and mean curvature. Graphical Models and Image Processing 58 (1996) 127–141CrossRefGoogle Scholar
  24. 24.
    Malladi R., Sethian J.A.: Level set methods for curvature flow, image enchancement and shape recovery in medical images. in Visualization and Mathematics: Experiments, Simulations, and Environments, Eds. H. C. Hege, K. Polthier, pp. 329–345, Springer Verlag, Heidelberg, 1997.Google Scholar
  25. 25.
    Malladi R., Sethian J.A.: A real-time algorithm for medical shape recovery. in Proceedings of ICCV’ 98, pp. 304–310, Mumbai India, January 1998.Google Scholar
  26. 26.
    Mikula K., Sarti A., Lamberti C.: Geometrical diffusion in 3D echocardiography. Proc. of ALGORITMY’ 97-Conference on Scientific Computing, West Tatra Mountains, Slovakia, 1997.Google Scholar
  27. 27.
    Miller F.R., Heppner G.H.: Cellular interactions in metastasis. Cancer and Metastasis Reviews 9 (1990) 21–34CrossRefGoogle Scholar
  28. 28.
    Mullikin J.C.: The vector distance transform in two and three dimensions. CVGIP: Graphical Models and Image Processing 54 (1992) 526–535CrossRefGoogle Scholar
  29. 29.
    Nordstrom N.K.: Variational edge detection. PhD dissertation, Department of electrical engineering, University of California, Berkeley, 1990Google Scholar
  30. 30.
    Ortiz de Solorzano C., Garcia Rodriguez E., Jones A., Pinkel D., Gray J.W., Sudar D., Lockett S.J.: Segmentation of confocal microscope images of cell nuclei in thick tissue sections. Journal of Microscopy 193 (1999) 212–226CrossRefGoogle Scholar
  31. 31.
    Ortiz de Solorzano C., Malladi R., Lelievre S., Lockett S.J.: Segmentation of Cell and Nuclei using Membrane Related Proteins. Journal of Microscopy-Oxford 201 (2001) 1–13CrossRefGoogle Scholar
  32. 32.
    Osher S.J., Sethian J.A.: Fronts propagation with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79 (1988) 12–49MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Rigaut J.P., Vassy J., Herlin P., Duigou F., Masson E., Briane D., Foucrier J., Carvajal-Gonzalez S., Downs A.M., Mandard A-M.: Three-Dimensional DNA Image Cytometry by Confocal Scanning Laser Microscopy in Thick Tissue Blocks. Cytometry 12 (1991) 511–524CrossRefGoogle Scholar
  34. 34.
    Sapiro G.: Color snakes. Hewlett-Packard Lab. tech report, 1995Google Scholar
  35. 35.
    Sapiro G., Kimmel R., Shaked D., Kimia B.B., Bruckstein A.M.: Implementing continuous-scale morphology via curve evolution. Pattern Recognition 6 (1993) 1363–1372CrossRefGoogle Scholar
  36. 36.
    Sarti A., Mikula K., Sgallari F.: Nonlinear multiscale analysis of 3D echocardiographic sequences IEEE Trans. on Medical Imaging 18 (1999) 453–466CrossRefGoogle Scholar
  37. 37.
    Sarti A., Ortiz de Solorzano C., Lockett S., Malladi R.: A Geometric Model for 3-D Confocal Image Analysis IEEE Trans. on Biomedical Engineering 47 (2000) 1600–1609CrossRefGoogle Scholar
  38. 38.
    Sethian J.A.: A review of recent numerical algorithms for hypersurfaces moving with curvature dependent flows. J. Differential Geometry 31 (1989) 131–161MathSciNetGoogle Scholar
  39. 39.
    Sethian J.A.: Level set methods: Evolving interfaces in geometry, fluid mechanics, computer vision, and material science. Cambridge University Press, 1997Google Scholar
  40. 40.
    Sochen N., Kimmel R., Malladi R.: A General Framework for Low Level Vision. IEEE Transactions on Image Processing, special issue on PDEs and Geometry-Driven Diffusion in Image Processing and Analysis 7 (1998) 310–318MathSciNetzbMATHGoogle Scholar
  41. 41.
    Wilson T.: Confocal Microscopy, Academic Press, London, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • C. Ortiz de Solorzano
    • 1
  • R. Malladi
    • 1
  • S. J. Lockett
    • 2
  1. 1.Lawrence Berkeley National LaboratoryUniversity of CaliforniaBerkeleyUSA
  2. 2.SAIC-FrederickFrederickUSA

Personalised recommendations