Bernhard Burgeth

1.7k total citations
31 papers, 712 citations indexed

About

Bernhard Burgeth is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Bernhard Burgeth has authored 31 papers receiving a total of 712 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Computer Vision and Pattern Recognition and 7 papers in Computational Mechanics. Recurrent topics in Bernhard Burgeth's work include Medical Image Segmentation Techniques (12 papers), Advanced Neuroimaging Techniques and Applications (9 papers) and MRI in cancer diagnosis (5 papers). Bernhard Burgeth is often cited by papers focused on Medical Image Segmentation Techniques (12 papers), Advanced Neuroimaging Techniques and Applications (9 papers) and MRI in cancer diagnosis (5 papers). Bernhard Burgeth collaborates with scholars based in Germany, United States and Netherlands. Bernhard Burgeth's co-authors include Joachim Weickert, Pavel Mrázek⋆, Thomas Brox, Michael Krause, Christoph Herrmann, Walter Krenkel, Stephan Didas, Martin Welk, Christian Feddern and Andreas Kleefeld and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Cancer and Journal of Materials Science.

In The Last Decade

Bernhard Burgeth

30 papers receiving 670 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Bernhard Burgeth Germany 15 336 189 79 79 74 31 712
M. Sabry Hassouna United States 10 320 1.0× 133 0.7× 38 0.5× 97 1.2× 25 0.3× 23 649
Jérôme Fehrenbach France 14 187 0.6× 97 0.5× 32 0.4× 48 0.6× 56 0.8× 34 840
Konstantinos K. Delibasis Greece 17 445 1.3× 343 1.8× 14 0.2× 78 1.0× 40 0.5× 91 984
J. Serra France 17 642 1.9× 42 0.2× 31 0.4× 28 0.4× 167 2.3× 58 1.1k
Jean‐Louis Dillenseger France 18 505 1.5× 311 1.6× 39 0.5× 43 0.5× 62 0.8× 89 979
Karl Krissian Spain 16 991 2.9× 367 1.9× 38 0.5× 170 2.2× 267 3.6× 31 1.3k
Mark Eramian Canada 12 282 0.8× 237 1.3× 17 0.2× 13 0.2× 107 1.4× 40 877
Andrei C. Jalba Netherlands 15 440 1.3× 77 0.4× 17 0.2× 187 2.4× 77 1.0× 44 755
Fabrice Mériaudeau France 9 245 0.7× 95 0.5× 31 0.4× 41 0.5× 25 0.3× 22 416
Yuping Duan China 17 470 1.4× 179 0.9× 12 0.2× 191 2.4× 89 1.2× 81 1.0k

Countries citing papers authored by Bernhard Burgeth

Since Specialization
Citations

This map shows the geographic impact of Bernhard Burgeth's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bernhard Burgeth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernhard Burgeth more than expected).

Fields of papers citing papers by Bernhard Burgeth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bernhard Burgeth. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bernhard Burgeth. The network helps show where Bernhard Burgeth may publish in the future.

Co-authorship network of co-authors of Bernhard Burgeth

This figure shows the co-authorship network connecting the top 25 collaborators of Bernhard Burgeth. A scholar is included among the top collaborators of Bernhard Burgeth based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bernhard Burgeth. Bernhard Burgeth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Burgeth, Bernhard, et al.. (2019). Mathematical Morphology and Its Applications to Signal and Image Processing. Lecture notes in computer science. 22 indexed citations
2.
Kleefeld, Andreas, et al.. (2018). Anomalous diffusion, dilation, and erosion in image processing. International Journal of Computer Mathematics. 95(6-7). 1375–1393. 1 indexed citations
3.
Lobbes, Marc B. I., Ivo Houben, Katja Pinker, et al.. (2016). Computer-aided diagnosis of diagnostically challenging lesions in breast MRI: a comparison between a radiomics and a feature-selective approach. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9871. 98710H–98710H. 1 indexed citations
4.
Westin, Carl‐Fredrik, Anna Vilanova, & Bernhard Burgeth. (2014). Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data. TU/e Research Portal. 20 indexed citations
5.
Burgeth, Bernhard & Andreas Kleefeld. (2014). An approach to color-morphology based on Einstein addition and Loewner order. Pattern Recognition Letters. 47. 29–39. 8 indexed citations
6.
7.
Krause, Michael, et al.. (2013). Fast retinal vessel analysis. Journal of Real-Time Image Processing. 11(2). 413–422. 34 indexed citations
8.
Moreno, Rodrigo, Miguel Ángel García, Domènec Puig, et al.. (2011). On Improving the Efficiency of Tensor Voting. IEEE Transactions on Pattern Analysis and Machine Intelligence. 33(11). 2215–2228. 16 indexed citations
9.
Ludwig, Nicole, Andreas Keller, Petra Leidinger, et al.. (2010). Novel immunogenic antigens increase classification accuracy in meningioma to 93.84%. International Journal of Cancer. 128(6). 1493–1501. 14 indexed citations
10.
Ludwig, Nicole, Andreas Keller, Petra Leidinger, et al.. (2009). Improving Seroreactivity-Based Detection of Glioma. Neoplasia. 11(12). 1383–1389. 19 indexed citations
11.
Didas, Stephan, Joachim Weickert, & Bernhard Burgeth. (2009). Properties of Higher Order Nonlinear Diffusion Filtering. Journal of Mathematical Imaging and Vision. 35(3). 208–226. 58 indexed citations
12.
Burgeth, Bernhard, Luis Pizarro, Michael Breuß, & Joachim Weickert. (2009). Adaptive Continuous-Scale Morphology for Matrix Fields. International Journal of Computer Vision. 92(2). 146–161. 2 indexed citations
13.
Krause, Michael, et al.. (2009). Determination of the fibre orientation in composites using the structure tensor and local X-ray transform. Journal of Materials Science. 45(4). 888–896. 134 indexed citations
14.
Bucur, Dorin, et al.. (2009). How to Choose Interpolation Data in Images. SIAM Journal on Applied Mathematics. 70(1). 333–352. 22 indexed citations
15.
Steidl, Gabriele, et al.. (2007). Restoration of matrix fields by second-order cone programming. Computing. 81(2-3). 161–178. 4 indexed citations
16.
Welk, Martin, Joachim Weickert, Florian Becker, et al.. (2006). Median and related local filters for tensor-valued images. Signal Processing. 87(2). 291–308. 27 indexed citations
17.
Feddern, Christian, Joachim Weickert, Bernhard Burgeth, & Martin Welk. (2006). Curvature-Driven PDE Methods for Matrix-Valued Images. International Journal of Computer Vision. 69(1). 93–107. 29 indexed citations
18.
Burgeth, Bernhard & Joachim Weickert. (2005). An Explanation for the Logarithmic Connection between Linear and Morphological System Theory. International Journal of Computer Vision. 64(2-3). 157–169. 8 indexed citations
19.
Brox, Thomas, Joachim Weickert, Bernhard Burgeth, & Pavel Mrázek⋆. (2005). Nonlinear structure tensors. Image and Vision Computing. 24(1). 41–55. 165 indexed citations
20.
Welk, Martin, Christian Feddern, Bernhard Burgeth, & Joachim Weickert. (2003). Median filtering of tensor-valued images. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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