Comparison of histomorphometrical data obtained with two different image analysis methods
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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.
KeywordsAutomatic Method Bone Area Visual Method Automatic Image Analysis Shadow Effect
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.
- 2.C. B. JOHANSSON, On tissue reactions to metal implants. PhD. Thesis, University of Göteborg, Biomaterials Group / Department of Handicap Research, Göteborg, Sweden (1991)Google Scholar
- 3.K. DONATH, Der Präparator 34 (1988) 197Google Scholar
- 4.J. LINDBLAD and E. BENGTSSON, in Proceedings of the 12th SCIA Scandinavian Conference on Image Analysis, Norway, June 2001, edited by I. Austvoll (NOBIM, 2001), p. 264Google Scholar
- 5.J. C. BEZDEK and R. J. HATHAWAY, in Proceedings of the 1st Institute of Electrical and Electronics Engineers, IEEE, Conf. on Evolutionary Computation, June 1994; edited by Z. Michalewicz et al. (IEEE, Piscataway, NJ, 1994) p. 589. Google Scholar
- 6.L. BALLERINI, L. BOCCHI and C. B. JOHANSSON, in Proceedings Application of Evolutionary Computation, LNCS 3005, 260-269, Portugal 2004Google Scholar
- 7.R. C. GONZALEZ and R. E. WOODS, in “Digital image processing” (2nd edn. Prentice Hall, Upper Saddle River, New Jersey, 2001) p. 649Google Scholar
- 9.L. VINCENT and P. SOILLE, Institute of Electrical and Electronics Engineers, IEEE. Trans. Pattern Recognit. Mach. Intel. 13 (1991) 583Google Scholar
- 10.P. BOLIND, On 606 retrieved oral and craniofacial implants. An analysis of consecutively received human specimens. PhD. Thesis, University of Göteborg, Department of Biomaterials /Handicap Research, Göteborg, Sweden (2004)Google Scholar
- 11.L.V. CARLSSON, On the development of a new concept for orthopaedic implant fixation. PhD Thesis, University of Göteborg, Biomaterials Group, Department of Handicap Research, Göteborg, Sweden (1989)Google Scholar