Tissue Reconstruction Based on Deformation of Dual Simplex Meshes
A new semiautomatic method for tissue reconstruction based on deformation of a dual simplex mesh was developed. The method is suitable for specifically-shaped objects. The method consists of three steps: the first step includes searching for object markers, i. e. the approximate centre of each object is localized. The searching procedure is based on careful analysis of object boundaries and on the assumption that the analyzed objects are sphere-like shaped. The first contribution of the method is the ability to find the markers without choosing the particular objects by hand.
In the next step the surface of each object is reconstructed. The procedure is based on the method for spherical object reconstruction presented in . The method was partially changed and was adapted to be more suitable for our purposes. The problem of getting stuck in local minima was solved. In addition, the deformation process was sped up.
The final step concerns quality evaluation: both of the first two steps are nearly automatic, therefore the quality of their results should be measured.
KeywordsDeformable models dual simplex mesh quality evaluation reconstruction
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- 5.Solorzano, C.O., Malladi, R., Lelievre, S.A., Lockett, S.J.: Segmentation of nuceli and cells using membrane related proteins markers. Journal of Microscopy 201, Pt 3, 404–415 (2001)Google Scholar
- 6.Kass, M., Witkin, A., Terzopoulos, D.: Active contour models. International Journal of Computer Vision 1(4), 133–144 (1987)Google Scholar
- 9.Kozubek, M.: High-resolution cytometry: Hardware approaches, image analysis techniques and applications, PhD thesis, Masaryk University, Brno (1998)Google Scholar
- 10.Gunn, S.R., Nixon, M.S.: A Dual Active Contour. In: BMVC 1994, York, U.K, pp. 305–314 (September 1994)Google Scholar
- 12.Devernay, F.: A non-maxima suppression method for edge detection with sub-pixel accuracy. Technical report, INRIA (1995)Google Scholar
- 13.Rodenacker, K., Aubele, M., Hutzler, P., Umesh Adiga, P.S.: Groping for quantitative digital 3-d image analysis: An approach to quantitative fluorescence in situ hybridization in thick tissue sections of prostate carcinoma. Anal. Cell Pathol. 15, 19–29 (1997)Google Scholar
- 14.Parker, J.R.: Algorithms for Image Processing and Computer Vision. John Wiley & Sons, New York (1997)Google Scholar