Influence of the boundary conditions on the result of non-linear image registration

Braumann, U.-D.; Kuska, Jens-Peer

doi:10.1109/icip.2005.1529954

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…This is done in two steps: first, we apply a rigid registration [13] and second, a nonlinear registration onto the ellipse [14, 15]. This minimizes the distortions produced by the nonlinear registration.…”

Section: Processing Pipelinementioning

confidence: 99%

Process flow for classification and clustering of fruit fly gene expression patterns

Heffel

Stadler

Prohaska

et al. 2008

2008 15th IEEE International Conference on Image Processing

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The rapidly growing collection of fruit fly embryo images makes automated Image Segmentation and classification an indispensable requirement for a large-scale analysis of in situ hybridization (ISH) -gene expression patterns (GEP). We present here such an automated process flow for Segmenting, Classification, and Clustering large-scale sets of Drosophila melanogaster GEP that is capable of dealing with most of the complications implicated in the images.

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Section: Processing Pipelinementioning

confidence: 99%

Process flow for classification and clustering of fruit fly gene expression patterns

Heffel

Stadler

Prohaska

et al. 2008

2008 15th IEEE International Conference on Image Processing

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show abstract

“…This is done in two steps: first, we apply a rigid registration [19] and second, a nonlinear registration onto the ellipse [20,21]. The found transformations onto an ellipse, computed from the registration of the embryo outlines, are then applied to the masked embryo images to obtain the expression pattern mapped onto an ellipse.…”

Section: Methodsmentioning

confidence: 99%

Automatic Classification of Embryonic Fruit Fly Gene Expression Patterns

Heffel

Prohaska

Stadler

et al. 2009

Bildverarbeitung Für Die Medizin 2009

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Abstract. Carefully studied in-situ hybridization Gene expression patterns (GEP) can provide a first glance at possible relationships among genes. Automatic comparative analysis tools are an indispensable requirement to manage the constantly growing amount of such GEP images. We present here an automated processing pipeline for Segmenting, Classification, and Clustering large-scale sets of Drosophila melanogaster GEP images that facilitates automatic GEP analysis.

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“…Once the segmentation was done for an image pair, we take the respective two scalar images to compute the displacement vector field for the nonlinear, nonparametric curvature-based registration (17). The latter requires the numerical solution of a coupled system of fourth-order partial differential equations.…”

Section: Image Registration and 3d Reconstruction And Tissue Segmentamentioning

confidence: 99%

Combined serial section‐based 3D reconstruction of cervical carcinoma invasion using H&E/p16^INK4a/CD3 alternate staining

Wentzensen

Braumann

Einenkel³

et al. 2007

Cytometry Pt A

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Background: Malignant growth and invasiveness of cancers is a function of both intratumoral and stromal factors. The accessibility to nutrients, oxygen and growth factors, the stromal composition, and the interference with the immune system all shape the tumor invasion front. A recent study has shown a prognostic difference with respect to different invasion patterns analyzed on histological specimens of cervical cancers. The present study analyzes the spatial organization of a cervical cancer and the relation of the tumor invasion front and the infiltration with CD31 T-cells. Methods: From a cervical squamous cell carcinoma specimen, 84 serial sections were performed and three interleaving series were stained with hematoxylin/eosin and immunohistochemistry directed against the cervical carcinoma biomarker p16INK4a and the T-cell marker CD3. Sections were passed through an image processing chain to obtain a reconstructed and segmented tissue volume. For local tumor invasion front analysis the mean curvature was used, which in turn was related to the respective local minimum tumor to T-cell distance as well to a T-cell originated diffusing substance's concentration at the tumor surface.

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Influence of the boundary conditions on the result of non-linear image registration

Cited by 11 publications

References 16 publications

Process flow for classification and clustering of fruit fly gene expression patterns

Process flow for classification and clustering of fruit fly gene expression patterns

Automatic Classification of Embryonic Fruit Fly Gene Expression Patterns

Combined serial section‐based 3D reconstruction of cervical carcinoma invasion using H&E/p16^INK4a/CD3 alternate staining

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Influence of the boundary conditions on the result of non-linear image registration

Cited by 11 publications

References 16 publications

Process flow for classification and clustering of fruit fly gene expression patterns

Process flow for classification and clustering of fruit fly gene expression patterns

Automatic Classification of Embryonic Fruit Fly Gene Expression Patterns

Combined serial section‐based 3D reconstruction of cervical carcinoma invasion using H&E/p16INK4a/CD3 alternate staining

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Product

Resources

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Combined serial section‐based 3D reconstruction of cervical carcinoma invasion using H&E/p16^INK4a/CD3 alternate staining