Skip to main content

Accuracy Evaluation for Region Centroid-Based Registration of Fluorescent CLSM Imagery


We present an accuracy evaluation of a semiautomatic registration technique for 3D volume reconstruction from fluorescent confocal laser scanning microscope (CLSM) imagery. The presented semiautomatic method is designed based on our observations that (a) an accurate point selection is much harder than an accurate region (segment) selection for a human, (b) a centroid selection of any region is less accurate by a human than by a computer, and (c) registration based on structural shape of a region rather than based on intensity-defined point is more robust to noise and to morphological deformation of features across stacks. We applied the method to image mosaicking and image alignment registration steps and evaluated its performance with 20 human subjects on CLSM images with stained blood vessels. Our experimental evaluation showed significant benefits of automation for 3D volume reconstruction in terms of achieved accuracy, consistency of results, and performance time. In addition, the results indicate that the differences between registration accuracy obtained by experts and by novices disappear with the proposed semiautomatic registration technique while the absolute registration accuracy increases.


  1. 1.

    Lee S-C, Bajcsy P: Feature based registration of fluorescent LSCM imagery using region centroids. Medical Imaging, February 2005, San Diego, Calif, USA, Proceedings of the SPIE 5747: 170–181.

    Google Scholar 

  2. 2.

    Collins CL, Ideker JH, Kurtis KE: Laser scanning confocal microscopy for in situ monitoring of alkali-silica reaction. Journal of Microscopy 2004, 213(2):149–157. 10.1111/j.1365-2818.2004.01280.x

    MathSciNet  Article  Google Scholar 

  3. 3.

    Pawley JB: The Handbook of Biological Confocal Microscopy. Plenum Press, New York, NY, USA; 1990.

    Google Scholar 

  4. 4.

    Zitova B, Flusser J: Image registration methods: a survey. Image and Vision Computing 2003, 21(11):977–1000. 10.1016/S0262-8856(03)00137-9

    Article  Google Scholar 

  5. 5.

    Hill DLG, Batchelor PG, Holden M, Hawkes DJ: Medical image registration. Physics in Medicine and Biology 2001, 46(3):R1–R45. 10.1088/0031-9155/46/3/201

    Article  Google Scholar 

  6. 6.

    Brown LG: A survey of image registration techniques. ACM Computing Surveys 1992, 24(4):325–376. 10.1145/146370.146374

    Article  Google Scholar 

  7. 7.

    Cohen LD, Cohen I: Deformable models for 3-D medical images using finite elements and balloons. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '92), June 1992, Champaign, Ill, USA 592–598.

    Google Scholar 

  8. 8.

    Goshtasby A: Registration of images with geometric distortions. IEEE Transactions on Geoscience and Remote Sensing 1988, 26(1):60–64. 10.1109/36.3000

    Article  Google Scholar 

  9. 9.

    Tuohy M, McConchie CA, Knox RB, Szarski L, Arkin A: Computer-assisted three-dimensional reconstruction technology in plant cell image analysis; applications of interactive computer graphics. Journal of Microscopy 1987, 147(1):83–88. 10.1111/j.1365-2818.1987.tb02820.x

    Article  Google Scholar 

  10. 10.

    Maintz JBA, Viergever MA: A survey of medical image registration. Medical Image Analysis 1998, 2(1):1–36.

    Article  Google Scholar 

  11. 11.

    Nocito A, Kononen J, Kallioniemi OP, Sauter G: Tissue microarrays (TMAs) for high-throughput molecular pathology research. International Journal of Cancer 2001, 94(1):1–5. 10.1002/ijc.1385

    Article  Google Scholar 

  12. 12.

    Goshtasby A, Stockman GC, Page CV: A region-based approach to digital image transformation with subpixel accuracy. IEEE Transactions on Geoscience and Remote Sensing 1986, 24(3):390–399.

    Article  Google Scholar 

  13. 13.

    Flusser J, Suk T: A moment-based approach to registration of images with affine geometric distortion. IEEE Transactions on Geoscience and Remote Sensing 1994, 32(2):382–387. 10.1109/36.295052

    Article  Google Scholar 

  14. 14.

    Li H, Manjunath BS, Mitra SK: A contour-based approach to multisensor image registration. IEEE Transactions on Image Processing 1995, 4(3):320–334. 10.1109/83.366480

    Article  Google Scholar 

  15. 15.

    Pratt WK: Correlation techniques of image registration. IEEE Transactions on Aerospace and Electronic Systems 1974, 10(3):353–358.

    Article  Google Scholar 

  16. 16.

    Viola P, Wells WM III: Alignment by maximization of mutual information. Proceedings of the 5th International Conference on Computer Vision (ICCV '95), June 1995, Cambridge, Mass, USA 16–23.

    Google Scholar 

  17. 17.

    Hermosillo G, Chefd'Hotel C, Faugeras O: Variational methods for multimodal image matching. International Journal of Computer Vision 2002, 50(3):329–343. 10.1023/A:1020830525823

    Article  Google Scholar 

  18. 18.

    Jungke M, Von Seelenon W, Bielke G, et al.: A system for the diagnostic use of tissue characterizing parameters in NMR-tomography. Proceedings of Information Processing in Medical Imaging (IPMI '87), 1987 39: 471–481.

    Google Scholar 

  19. 19.

    Montgomery K, Ross MD: Non fiducial, shaped-based registration of biological tissue. Three-Dimensional Microscopy: Image Acquisition and Processing III, January 1996, San Jose, Calif, USA, Proceedings of SPIE 2655: 224–232.

    Google Scholar 

  20. 20.

    Goulden CH: Methods of Statistical Analysis. 2nd edition. John Wiley & Sons, New York, NY, USA; 1956.

    Google Scholar 

  21. 21.

    Kooper R, Shirk A, Lee S-C, Lin A, Folberg R, Bajcsy P: 3D medical volume reconstruction using Web services. Proceedings of IEEE International Conference on Web Services (ICWS '05), July 2005, Orlando, Fla, USA 716.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Sang-Chul Lee.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lee, S., Bajcsy, P., Lin, A. et al. Accuracy Evaluation for Region Centroid-Based Registration of Fluorescent CLSM Imagery. EURASIP J. Adv. Signal Process. 2006, 082480 (2006).

Download citation


  • Confocal Laser Scanning Microscope
  • Accuracy Evaluation
  • Structural Shape
  • Point Selection
  • Accuracy Increase