Skip to main content
SpringerLink
Log in

Novel Computerized Methods in System Biology –Flexible High-Content Image Analysis and Interpretation System for Cell Images

  • Conference paper
Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry (MDA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5108))

Abstract

In the rapidly expanding fields of cellular and molecular biology, fluorescence illumination and observation is becoming one of the techniques of choice to study the localization and dynamics of proteins, organelles, and other cellular compartments, as well as a tracer of intracellular protein trafficking. The automatic analysis of these images and signals in medicine, biotechnology, and chemistry is a challenging and demanding field. Signal-producing procedures by microscopes, spectrometers and other sensors have found their way into wide fields of medicine, biotechnology, industrial and environmental analysis. With this arises the problem of the automatic mass analysis of signal information. Signal-interpreting systems which automatically generate the desired target statements from the signals are therefore of compelling necessity. The continuation of mass analysis on the basis of the classical procedures leads to investments of proportions that are not feasible. New procedures and system architectures are therefore required. We will present, based on our flexible image analysis and interpretation system Cell_Interpret, new intelligent and automatic image analysis and interpretation procedures. We will demonstrate it in the application of the HEp-2 cell pattern analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Perner, P.: Data Mining on Multimedia Data. LNCS, vol. 2558. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  2. Perner, P.: Utility model, computer system for the automatic data analysis, classification, interpretation and data mining of cells, cell structures, microorganism, biotic particle, parts and products in digital images, DE 20206003294 U1

    Google Scholar 

  3. Wang, L., Bai, J.: Threshold selection by clustering gray levels of boundary. Pattern Recognition Letters 24(12), 1983–1999 (2003)

    Article  MathSciNet  Google Scholar 

  4. Demirkaya, O., Asyali, M.H.: Determination of image bimodality thresholds for different intensity distributions. Signal Processing: Image Communication 19(6), 507–516 (2004)

    Article  Google Scholar 

  5. Patricio, M.A., Maravall, D.: A novel generalization of the gray-scale histogram and its application to the automated visual measurement and inspection of wooden Pallets. Image and Vision Computing, 2006 25(6), 805–816 (2007)

    Article  Google Scholar 

  6. Pauwels, E.J., Frederix, G.: Finding Salient Regions in Images: Nonparametric Clustering for Image Segmentation and Grouping. Computer Vision and Image Understanding 75(1-2), 73–85 (1999)

    Article  Google Scholar 

  7. Cutrona, J., Bonnet, N., Herbin, M., Hofer, F.: Advances in the segmentation of multi-component microanalytical images. Ultramicroscopy 103(2), 141–152 (2005)

    Article  Google Scholar 

  8. Filin, S., Pfeifer, N.: Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS Journal of Photogrammetry and Remote Sensing 60(2), 71–80 (2006)

    Article  Google Scholar 

  9. Kermad, C.D., Chehdi, K.: Automatic image segmentation system through iterative edge–region co-operation. Image and Vision Computing 20(8), 541–555 (2002)

    Article  Google Scholar 

  10. Muñoz, X., Freixenet, J., Cufí, X., Martí, J.: Strategies for image segmentation combining region and boundary information. Pattern Recognition Letters 24(1-3), 375–392 (2003)

    Article  Google Scholar 

  11. Voss, T.C., Demarco, I.A., Day, R.N.: Quantitative Imaging of Protein Interactions in the cell nucleus. Biotechniques 38(3), 413–424 (2005)

    Article  Google Scholar 

  12. Beucher, S., Meyer, F.: The morphological approach of segmentation: the watershed transformation. In: Dougherty, E. (ed.) Mathematical Morphology in Image Processing, pp. 433–481. Marcel Dekker, New York (1993)

    Google Scholar 

  13. Perner, P.: An architecture for a CBR image segmentation system. Journal of Engineering Application in Artificial Intelligence 12(6), 749–759 (1999)

    Article  Google Scholar 

  14. Frucci, M., Perner, P., Sanniti di Baja, G.: Case-based Reasoning for Image Segmentation by Watershed Transformation. In: Perner, P. (ed.) Case-Based Reasoning on Signals and Images. Springer, Heidelberg (2007)

    Google Scholar 

  15. Grimnes, M., Aamodt, A.: A two layer case-based reasoning architecture for medical image understanding. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 164–178. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  16. Knowles, D.W., Sudar, D., Bator-Kelly, C., Bissell, M.J., Lelievre, S.A.: Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype. PNAS 103(12), 445–4445 (2006)

    Article  Google Scholar 

  17. Perner, P., Perner, H., Jänichen, S.: Recognition of Airborne Fungi Spores in Digital Microscopic Images. Journal Artificial Intelligence in Medicine AIM, Special Issue on CBR 36(2), 137–157 (2006)

    Article  Google Scholar 

  18. Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. John Wiley & Sons, Chichester (1998)

    MATH  Google Scholar 

  19. Jaenichen, S., Perner, P.: Conceptual Clustering and Case Generalization of two-dimensional Forms. Computational Intelligence 22(3/4), 178–193 (2006)

    Google Scholar 

  20. Zamperoni, P.: Feature Extraction. In: Maitre, H., Zinn-Justin, J. (eds.) Progress in Picture Processing, pp. 121–184. Elsevier Science, Amsterdam (1996)

    Google Scholar 

  21. Perner, P., Perner, H., Müller, B.: Mining Knowledge for Hep-2 Cell Image Classification. Journal Artificial Intelligence in Medicine 26, 161–173 (2002)

    Article  Google Scholar 

  22. Perner, P.: Prototype-Based Classification Applied Intelligence (to appear) (online available)

    Google Scholar 

  23. Chang, C.-L.: Finding Prototypes for Nearest Neighbor Classifiers. IEEE Trans. on Computers C-23(11), 1179–1184 (1974)

    Article  Google Scholar 

  24. Wettschereck, D., Aha, D.W.: Weighting Features. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 347–358. Springer, Heidelberg (1995)

    Chapter  Google Scholar 

  25. Perner, P.: Image Mining: Issues, framework, a generic tool and its application to medical-image diagnosis. Journal Engineering Applications of Artificial Intelligence 15(2), 193–203

    Google Scholar 

  26. Perner, P., Perner, H., Müller, B.: Texture Classification based on Random Sets and its Application to Hep-2 Cells. In: Kasturi, R., Laurendeau, D., Suen, C. (eds.) ICPR 2002, vol. II, pp. 406–411. IEEE Computer Society, Los Alamitos (2002)

    Google Scholar 

  27. Gokay, K.E., Wilson, J.M.: Targeting of an Apical Endosomal Protein to Endosomes in Madin–Darby Canine Kidney Cells Requires Two Sorting Motifs. Traffic 1, 354–365 (2000)

    Article  Google Scholar 

  28. Beil, M., Dürschmied, D., Paschke, St., Schreiner, B., Nolte, U., Bruel, A., Irinopoulou, T.: Spatial Distribution Patterns of Interphase Centromeres During Retinoic Acid-Induced Differentiation of Promyelocytic Leukemia Cells. Cytometry 47, 217–225 (2002)

    Article  Google Scholar 

  29. Velliste, M., Murphy, R.F.: Automated determination of protein subcellular locations from 3D fluorescence microscope images. In: Proc. Biomedical Imaging 2002, pp. 867–870. IEEE Press, Los Alamitos (2002)

    Chapter  Google Scholar 

  30. Irinopoulou, T., Vassy, J., Beil, M., Nicol, P.: Three-Dimensional DNA Image Cytometry by Confocal Scanning Laser Microscopy in Thick Tissue Blocks of Prostatic Lesions. Cytometry 27, 99–105 (1997)

    Article  Google Scholar 

  31. Swedlow, J.R., Goldberg, I., Brauner, E., Sorger, P.K.: Informatics and Quantitative Analysis in Biological Imaging. Science 300(5616), 100–102 (2003)

    Article  Google Scholar 

  32. Tran, D., Pham, T., Zhou, X.: Cell Phase Indentification using Fuzzy Gaussian Mixture Models. In: ISPACS 2005, International Symposium on Intelligent Signal Processing and Communication Systems, Hong Kong, China, December 14-17, 2005, pp. 465–468 (2005)

    Google Scholar 

  33. Lieb, J.D., Ortiz de Solorzano, C., Garcia Rodriguez, E., Jones, A., Angelo, M., Lockett, S., Meyer, B.J.: The Caenorhabditis elegans Dosage Compensation Machinery Is Recruited to X Chromosome DNA Attached to an Autosome. Genetics 156, 1603–1621 (2000)

    Google Scholar 

  34. Ecker, R.C., Steiner, G.E.: Microscopy-Based Multicolor Tissue Cytometry at the Single-Cell Level. Cytometry Part A 59A, 182–190 (2004)

    Article  Google Scholar 

  35. Swedlow, J.R., Goldberg, I., Brauner, E., Sorger, P.K.: Informatics and Quantitative Analysis in Biological Imaging. Science 300(5616), 100–102 (2003)

    Article  Google Scholar 

  36. Berlage, T.: Analyzing and mining image databases. DDT 10(11), 795–802 (2005)

    Google Scholar 

  37. Perner, P., Holt, A., Richter, M.: Image Processing in Case-Based Reasoning. The Knowledge Engineering Review 20(3), 311–331

    Google Scholar 

  38. De Mantaras, R.L., Cunningham, P., Perner, P.: Emergent case-based reasoning applications. The Knowledge Engineering Review 20(3), 325–328

    Google Scholar 

  39. Holt, A., Bichindaritz, I., Schmidt, R., Perner, P.: Medical applications in case-based reasoning. The Knowledge Engineering Review 20(3), 289–292

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Vision and applied Computer Sciences, IBaI, Arno-Nitzsche-Str. 43, 04277, Leipzig,  

    Petra Perner

Authors
  1. Petra Perner
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Petra Perner Ovidio Salvetti

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Perner, P. (2008). Novel Computerized Methods in System Biology –Flexible High-Content Image Analysis and Interpretation System for Cell Images. In: Perner, P., Salvetti, O. (eds) Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry. MDA 2008. Lecture Notes in Computer Science(), vol 5108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70715-8_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-70715-8_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70714-1

  • Online ISBN: 978-3-540-70715-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation