Computer Science - Research and Development

, Volume 26, Issue 1–2, pp 125–134 | Cite as

A non-linear multi-scale blending algorithm for fluorescence bladder images

  • Alexander BehrensEmail author
  • Martin Guski
  • Thomas Stehle
  • Sebastian Gross
  • Til Aach
Special Issue Paper


The composition of panoramic images of the internal urinary bladder wall from single endoscope images can strongly support the off-line documentation and surgery planning for cancer treatment, as well as assist the re-identification of multi-focal tumors during a cystoscopy. Unlike white light endoscopy, fluorescence techniques such as photodynamic diagnostics (PDD) lower the risk of missing flat and small tumors due to an enhanced tissue contrast. As a result of the low illumination power and the free hand movement of the endoscope, PDD video sequences usually show strong variations in illumination and resolution. During the subsequent mosaicking and blending process, these effects impede the preservation of original and unbiased input image information in the composed panoramic overview image, and make it difficult to avoid visual interpolation artifacts. Thus, a non-linear intensity based multi-scale blending method for fluorescence images is developed. Based on a highest intensity decision, a region mask modeling the endoscopic illumination characteristic is used to weight the input images on several sub-bands of a Laplacian pyramid. In comparison to basic linear interpolation and standard multi-scale methods the new method preserves high fluorescence intensities as well as fine vessel structures in the final image composition. Furthermore the adaptive characteristics of the blending algorithm allow the physician to move the endoscope more freely along the bladder wall during the image mosaicking process.


Blending Interpolation Image mosaicking Panorama Fluorescence endoscopy Bladder cancer 


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  1. 1.
    American Cancer Society (2010) Cancer facts and figures Google Scholar
  2. 2.
    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Surf: Speeded up robust features. Comput Vis Image Underst (CVIU) 110(3):346–359 CrossRefGoogle Scholar
  3. 3.
    Behrens A, Bommes M, Stehle T, Gross S, Leonhardt S, Aach T (2010) A multi-threaded mosaicking algorithm for fast image composition of fluorescence bladder images. In: Proc SPIE medical imaging 2010: visualization, image-guided procedures and modeling, vol 7625, p 76252S Google Scholar
  4. 4.
    Behrens A, Bommes M, Stehle T, Gross S, Leonhardt S, Aach T (2010) Real-time image composition of bladder mosaics in fluorescence endoscopy. Comput Sci Res Dev Med Image Process. doi: 10.1007/s00450-010-0135-z Google Scholar
  5. 5.
    Behrens A, Guski M, Stehle T, Gross S, Aach T (2010) Intensitätsbasiertes Multiskalen-Blending zur Erstellung von Panoramabildern in der Fluoreszenzendoskopie. In: Bildverarbeitung für die Medizin 2010, vol 574. Springer, Berlin, pp 51–55 Google Scholar
  6. 6.
    Behrens A, Guski M, Stehle T, Gross S, Aach T (2010) Intensity based multi-scale blending for panoramic images in fluorescence endoscopy. In: Proc IEEE int symp on biomedical imaging (ISBI), pp 1305–1308 Google Scholar
  7. 7.
    Bergen T, Ruthotto S, Munzenmayer C, Rupp S, Paulus D, Winter C (2009) Feature-based real-time endoscopic mosaicking. In: Proc 6th int symp image and signal processing and analysis (ISPA), pp 695–700 Google Scholar
  8. 8.
    Blinn J (1994) Compositing. 1. Theory. IEEE Comput Graph Appl 14(5):83–87 CrossRefGoogle Scholar
  9. 9.
    Blum H (1967) A transformation for extracting new descriptors of shape. In: Wathen-Dunn W (ed) Models for the perception of speech and visual form. MIT Press, Cambridge, pp 362–380 Google Scholar
  10. 10.
    Burt P, Adelson E (1983) A multiresolution spline with application to image mosaics. ACM Trans Graph 2(4):217–236 CrossRefGoogle Scholar
  11. 11.
    Cao CG, Milgram P (2000) Disorientation in minimal access surgery: A case study. In: Proc IEA 2000/HFES congress, vol 4, pp 169–172 Google Scholar
  12. 12.
    Chen CY, Klette R (1999) Image stitching—comparisons and new techniques. In: Computer analysis of images and patterns (CAIP). Lecture notes in computer science, vol 1689. Springer, Berlin, pp 615–622 CrossRefGoogle Scholar
  13. 13.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395 CrossRefMathSciNetGoogle Scholar
  14. 14.
    Gross S, Behrens A, Stehle T (2009) Rapid development of video processing algorithms with RealTimeFrame. In: Proc biomedica, pp 217–220 Google Scholar
  15. 15.
    Gross S, Stehle T (2008) RealTimeFrame—a real time processing framework for medical video sequences. Acta Polytech J Adv Eng 48(3):15–19 Google Scholar
  16. 16.
    Hudson MA, Herr HW (1995) Carcinoma in situ of the bladder. J Urol 153:564–572 Google Scholar
  17. 17.
    Hungerhuber E, Stepp H, Kriegmair M, Stief C, Hofstetter A, Hartmann A, Knuechel R, Karl A, Tritschler S, Zaak D (2007) Seven years’ experience with 5-aminolevulinic acid in detection of transitional cell carcinoma of the bladder. Urology 69(2):260–264 CrossRefGoogle Scholar
  18. 18.
    Miranda-Luna R, Daul C, Blondel W, Hernandez-Mier Y, Wolf D, Guillemin F (2008) Mosaicking of bladder endoscopic image sequences: distortion calibration and registration algorithm. IEEE Trans Biomed Eng 55(2):541–553 Google Scholar
  19. 19.
    Miranda-Luna R, Hernandez-Mier Y, Daul C, Blondel W, Wolf D (2004) Mosaicing of medical video-endoscopic images: data quality improvement and algorithm testing. In: 1st int conf electrical and electronics engineering (ICEEE), pp 530–535 CrossRefGoogle Scholar
  20. 20.
    Olijnyk S, Mier YH, Blondel WM, Daul C, Wolf D, Bourg-Heckly G (2007) Combination of panoramic and fluorescence endoscopic images to obtain tumor spatial distribution information useful for bladder cancer detection. In: Proc SPIE novel optical instrumentation for biomedical applications III, vol 6631, p 66310X Google Scholar
  21. 21.
    Orozco RE, Martin AA, Murphy WM (1994) Carcinoma in situ of the urinary bladder, clues to host involvement in human carcinogenesis. Cancer 74(1):115–122 CrossRefGoogle Scholar
  22. 22.
    Porter T, Duff T (1984) Compositing digital images. In: Proc of 11th annu conf on computer graphics and interactive techniques (SIGGRAPH), pp 253–259 CrossRefGoogle Scholar
  23. 23.
    Szeliski R (2006) Image alignment and stitching: a tutorial. Tech. Rep. MSR-TR-2004-92, Microsoft Research Google Scholar
  24. 24.
    Szeliski R, Shum HY (1997) Creating full view panoramic image mosaics and environment maps. In: Proc of 24th annu conf on computer graphics and interactive techniques (SIGGRAPH), pp 251–258 CrossRefGoogle Scholar
  25. 25.
    Uyttendaele M, Eden A, Skeliski R (2001) Eliminating ghosting and exposure artifacts in image mosaics. In: Proc of the IEEE computer society conference on computer vision and pattern recognition (CVPR), vol 2, pp 509–516 Google Scholar
  26. 26.
    Wald D, Reeff M, Székely G, Cattin P, Paulus D (2005) Fließende Überblendung von Endoskopiebildern für die Erstellung eines Mosaiks. In: Bildverarbeitung für die Medizin. Springer, Berlin, pp 287–291 CrossRefGoogle Scholar
  27. 27.
    Wickham JEA (1987) The new surgery. Br Med J 295(6613):1581–1582 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Alexander Behrens
    • 1
    Email author
  • Martin Guski
    • 1
  • Thomas Stehle
    • 1
  • Sebastian Gross
    • 1
  • Til Aach
    • 1
  1. 1.Institute of Imaging & Computer VisionRWTH Aachen UniversityAachenGermany

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