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Comparison of histomorphometrical data obtained with two different image analysis methods

  • Lucia Ballerini
  • Victoria Franke-Stenport
  • Gunilla Borgefors
  • Carina B. Johansson
Article

Abstract

A common way to determine tissue acceptance of biomaterials is to perform histomorphometrical analysis on histologically stained sections from retrieved samples with surrounding tissue, using various methods. The “time and money consuming” methods and techniques used are often “in house standards”. We address light microscopic investigations of bone tissue reactions on un-decalcified cut and ground sections of threaded implants. In order to screen sections and generate results faster, the aim of this pilot project was to compare results generated with the in-house standard visual image analysis tool (i.e., quantifications and judgements done by the naked eye) with a custom made automatic image analysis program. The histomorphometrical bone area measurements revealed no significant differences between the methods but the results of the bony contacts varied significantly. The raw results were in relative agreement, i.e., the values from the two methods were proportional to each other: low bony contact values in the visual method corresponded to low values with the automatic method. With similar resolution images and further improvements of the automatic method this difference should become insignificant. A great advantage using the new automatic image analysis method is that it is time saving—analysis time can be significantly reduced.

Keywords

Automatic Method Bone Area Visual Method Automatic Image Analysis Shadow Effect 
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.

Notes

Acknowledgements

Research technicians Petra Johansson and Maria Hoffman, Department of Biomaterials/Handicap Research, Göteborg University, are greatly acknowledged for their skill in the cutting and grinding- and image acquisition techniques. Dr. Joakim Lindblad, Centre for Image Analysis, Uppsala, is gladly acknowledged for sharing his algorithms and for very fruitful suggestions. This work was supported by grants from the Swedish Research Council, no 621-2005-3402.

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Lucia Ballerini
    • 1
  • Victoria Franke-Stenport
    • 3
  • Gunilla Borgefors
    • 2
  • Carina B. Johansson
    • 1
  1. 1.Department of Clinical Medicine/Medical TechnologyÖrebro UniversityOrebroSweden
  2. 2.Centre for Image AnalysisSwedish University of Agricultural SciencesUppsalaSweden
  3. 3.Department of Prosthetic Dentistry/Dental Materials Science, Inst. of Odontology and Department of Biomaterials, Inst. of Surgical SciencesThe Sahlgrenska Academy at Göteborg UniversityGoteborgSweden

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