Thomas Pralow
Impact in
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- Image and Signal Denoising Methods
- Medical Image Segmentation Techniques
- Image and Object Detection Techniques
Papers in
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- Medical Image Segmentation Techniques 2
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- AI in cancer detection 2
- Gaussian Processes and Bayesian Inference 1
- Co-authors
- Rolf‐Rainer Grigat (5 shared papers)Ulrich Neitzel (1 shared paper)Mathias Prokop (1 shared paper)Cornelia Schaefer‐Prokop (1 shared paper)
- Journals
- Computer Science - Research and Development (1 paper)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2 papers)tub.dok (Hamburg University of Technology) (2 papers)
In The Last Decade
Thomas Pralow
6 papers receiving 31 citations
Peers
Comparison fields: 5 of 22
- Health Informatics 1
- Computer Vision and Pattern Recognition 14
- Human-Computer Interaction 3
- Radiology, Nuclear Medicine and Imaging 9
- Biomedical Engineering 15
Countries citing papers authored by Thomas Pralow
This map shows the geographic impact of Thomas Pralow'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 Thomas Pralow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Pralow more than expected).
Fields of papers citing papers by Thomas Pralow
This network shows the impact of papers produced by Thomas Pralow. 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 Thomas Pralow. The network helps show where Thomas Pralow may publish in the future.
Co-authors
The 4 scholars most cited alongside Thomas Pralow, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Modeling and real-time estimation of signal-dependent noise in quantum-limited imaging | 2007 | 15 |
| 2 | 2010 | 9 | |
| 3 | Model-Based Lower Limb Segmentation using Weighted Multiple Candidates. | 2010 | 4 |
| 4 | 1998 | 3 | |
| 5 | 2009 | 2 | |
| 6 | 2009 | 1 |
About Thomas Pralow
Thomas Pralow is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Pulmonary and Respiratory Medicine, having authored 6 papers that have together received 34 indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (3 papers), Medical Imaging and Analysis (3 papers), Medical Image Segmentation Techniques (2 papers), AI in cancer detection (2 papers), Dental Radiography and Imaging (1 paper), Digital Radiography and Breast Imaging (1 paper), Radiation Dose and Imaging (1 paper) and Gaussian Processes and Bayesian Inference (1 paper). The work is most often cited by research in Health Informatics (1 citation), Computer Vision and Pattern Recognition (14 citations), Human-Computer Interaction (3 citations), Radiology, Nuclear Medicine and Imaging (9 citations) and Biomedical Engineering (15 citations). Thomas Pralow has collaborated with scholars based in Germany and Finland. Frequent co-authors include Rolf‐Rainer Grigat, Ulrich Neitzel, Mathias Prokop and Cornelia Schaefer‐Prokop. Their work appears in journals such as Computer Science - Research and Development, Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE and tub.dok (Hamburg University of Technology).
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.