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Generally speaking, a new method for the non-linear registration of a stack of slice images applying several runs with alternating directions in the stack has been introduced. The new method has shown to work well even under the presence of diffuse cancer structures as appearing in BCC. 4 Discussion This work has successfully given a proof-of-principle for an image processing chain for a 3D-reconstruction of BCC. The starting point was an H&E stained large serial histological section from paraffin embedded specimen.

Therefore, an overall (3D) segmentation of the colour space is not possible, because the same colour can correspond to different tissue types in different slices due to different slice thickness and exposure time to the staining chemicals. The fuzzy c-means segmentation does solve this problem by an adaptation of already calculated segmentation-classdistributions and therefore provides constantly good tissue segmentation results throughout the whole image stack. 28 P. Scheibe et al. (a) (b) (c) Fig.

An efficient locally affine framework for the smooth registration of anatomical structures. Medical Image Analysis 12(4), 427–441 (2008) 18. : Landmark-based image analysis: using geometric and intensity models. , Dordrecht (2001) 19. : Image Matching as a Diffusion Process: An Analogy with Maxwell’s Demons. Medical Image Analysis 2(3), 243–260 (1998) 20. : Understanding the Demon’s Algorithm: 3D Non-Rigid Registration by Gradient Descent. , Colchester, A. ) MICCAI 1999. LNCS, vol. 1679, pp. 597–605.

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