Search Results - "Nattkemper, T.W."

  • Showing 1 - 16 results of 16
Refine Results
  1. 1

    Categorization of two-photon microscopy images of human cartilage into states of osteoarthritis by Bergmann, T, Maeder, U, Fiebich, M, Dickob, M, Nattkemper, T.W, Anselmetti, D

    Published in Osteoarthritis and cartilage (01-08-2013)
    “…Summary Objective The degeneration of articular cartilage is part of the clinical syndrome of osteoarthritis (OA) and one of the most common causes of pain and…”
    Get full text
    Journal Article
  2. 2

    An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data by Twellmann, T., Lichte, O., Nattkemper, T.W.

    Published in IEEE transactions on medical imaging (01-10-2005)
    “…Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the…”
    Get full text
    Journal Article
  3. 3

    A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections by Nattkemper, T.W., Ritter, H.J., Schubert, W.

    “…A neural cell detection system (NCDS) for the automatic quantitation of fluorescent lymphocytes in tissue sections is presented in this paper. The system…”
    Get full text
    Journal Article
  4. 4

    Identification of Genes Relevant to Symbiosis and Competitiveness in Sinorhizobium meliloti Using Signature-Tagged Mutants by Pobigaylo, N, Szymczak, S, Nattkemper, T.W, Becker, A

    Published in Molecular plant-microbe interactions (01-02-2008)
    “…Sinorhizobium meliloti enters an endosymbiosis with alfalfa plants through the formation of nitrogen-fixing nodules. In order to identify S. meliloti genes…”
    Get full text
    Journal Article
  5. 5

    A method for linking computed image features to histological semantics in neuropathology by Lessmann, B., Nattkemper, T.W., Hans, V.H., Degenhard, A.

    Published in Journal of biomedical informatics (01-12-2007)
    “…In medical image analysis the image content is often represented by features computed from the pixel matrix in order to support the development of improved…”
    Get full text
    Journal Article
  6. 6

    CellViCAM—Cell viability classification for animal cell cultures using dark field micrographs by Burgemeister, S., Nattkemper, T.W., Noll, T., Hoffrogge, R., Flaschel, E.

    Published in Journal of biotechnology (15-09-2010)
    “…Online monitoring of cell density and cell viability is a challenging but essential task to control and optimize biotechnical processes and is of particular…”
    Get full text
    Journal Article Conference Proceeding
  7. 7

    BIIGLE Tools - A Web 2.0 Approach for Visual Bioimage Database Mining by Schoening, T., Ehnert, N., Ontrup, J., Nattkemper, T.W.

    “…In this paper we want to discuss the usage of Web 2.0 techniques to realize information visualization based exploration and annotation of huge volume,…”
    Get full text
    Conference Proceeding
  8. 8

    Flexible synapse detection in fluorescence micrographs by modeling human expert grading by Herold, J., Friedenberger, M., Bode, M., Rajpoot, N., Schubert, W., Nattkemper, T.W.

    “…A particularly difficult task in molecular imaging is the analysis of fluorescence microscopy images of neural tissue, as they usually exhibit a high density…”
    Get full text
    Conference Proceeding
  9. 9

    Biigle - Web 2.0 enabled labelling and exploring of images from the Arctic deep-sea observatory HAUSGARTEN by Ontrup, J., Ehnert, N., Bergmann, M., Nattkemper, T.W.

    Published in OCEANS 2009-EUROPE (01-05-2009)
    “…Deep-sea research relies strongly on the use of high-resolution cameras which generate large quantities of footage. The material can currently, however, often…”
    Get full text
    Conference Proceeding
  10. 10

    Evaluation of multiparameter micrograph analysis with synthetical benchmark images by Nattkemper, T.W., Saalbach, A., Twellmann, T.

    “…To analyze multiparametric images of biological systems in vivo, advanced image processing, visualization and data mining tools are under development. To…”
    Get full text
    Conference Proceeding
  11. 11

    Breast MRI data analysis by LLE by Varini, C., Nattkemper, T.W., Degenhard, A., Wismuller, A.

    “…Locally linear embedding (LLE) has recently been proposed as a powerful algorithm for unsupervised learning and dimensional data reduction. For a first time we…”
    Get full text
    Conference Proceeding
  12. 12

    Detection of suspicious lesions in dynamic contrast enhanced MRI data by Twellmann, T., Saalbach, A., Muller, C., Nattkemper, T.W., Wismuller, A.

    “…Dynamic contrast-enhanced magnet resonance imaging (DCE-MRI) has become an important source of information to aid breast cancer diagnosis. Nevertheless, next…”
    Get full text
    Conference Proceeding
  13. 13

    Look & listen: sonification and visualization of multiparameter micrographs by Nattkemper, T.W., Hermann, T., Schubert, W., Ritter, H.

    “…Multiparameter imaging techniques provide large numbers of high-dimensional image data in modern biomedical research. Besides algorithms for image…”
    Get full text
    Conference Proceeding
  14. 14

    A MeSH term based distance measure for document retrieval and labeling assistance by Ontrup, J., Nattkemper, T.W., Gerstung, O., Ritter, H.

    “…For biomedical and pharmaceutical research, the PUBMED database of the NLM (National Library of Medicine) has become a viable platform. It provides the means…”
    Get full text
    Conference Proceeding
  15. 15

    A machine learning based system for multichannel fluorescence analysis in pancreatic tissue bioimages by Herold, J., Abouna, S., Luxian Zhou, Pelengaris, S., Epstein, D.B.A., Khan, M., Nattkemper, T.W.

    “…Fluorescence microscopy has regained much attention in the last years especially in the field of systems biology. It has been recognized as a rich source of…”
    Get full text
    Conference Proceeding Journal Article
  16. 16

    Fluorescence micrograph segmentation by gestalt-based feature binding by Nattkemper, T.W., Wersing, H., Schubert, W., Ritter, H.

    “…We present the application of a recurrent neural network feature binding model to the segmentation of fluorescence micrographs, images showing fluorescent…”
    Get full text
    Conference Proceeding