Ewa Piętka

2.1k total citations
81 papers, 1.3k citations indexed

About

Ewa Piętka is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Ewa Piętka has authored 81 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 26 papers in Radiology, Nuclear Medicine and Imaging and 19 papers in Biomedical Engineering. Recurrent topics in Ewa Piętka's work include Medical Image Segmentation Techniques (21 papers), Medical Imaging Techniques and Applications (13 papers) and Forensic Anthropology and Bioarchaeology Studies (13 papers). Ewa Piętka is often cited by papers focused on Medical Image Segmentation Techniques (21 papers), Medical Imaging Techniques and Applications (13 papers) and Forensic Anthropology and Bioarchaeology Studies (13 papers). Ewa Piętka collaborates with scholars based in Poland, United States and Switzerland. Ewa Piętka's co-authors include H. K. Huang, Vicente Gilsanz, Jacek Kawa, Fei Cao, Arkadiusz Gertych, Paweł Badura, Min‐Liang Kuo, H. K. Huang, M. Ines Boechat and Stefano Mora and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Ewa Piętka

78 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ewa Piętka Poland 18 402 401 333 289 261 81 1.3k
Ching‐Wei Wang Taiwan 23 333 0.8× 155 0.4× 402 1.2× 326 1.1× 567 2.2× 82 1.8k
Hyunkwang Lee United States 13 419 1.0× 175 0.4× 450 1.4× 270 0.9× 451 1.7× 15 1.7k
Abhishek Gupta India 18 200 0.5× 60 0.1× 197 0.6× 181 0.6× 153 0.6× 68 1.1k
Darko Štern Austria 17 157 0.4× 210 0.5× 221 0.7× 407 1.4× 103 0.4× 34 960
Zhongke Wu China 18 622 1.5× 146 0.4× 95 0.3× 124 0.4× 76 0.3× 157 1.1k
João L. Vilaça Portugal 21 332 0.8× 26 0.1× 349 1.0× 359 1.2× 101 0.4× 155 1.6k
Fuqing Duan China 15 531 1.3× 89 0.2× 65 0.2× 90 0.3× 86 0.3× 98 853
Marius George Linguraru United States 26 805 2.0× 23 0.1× 853 2.6× 517 1.8× 440 1.7× 209 2.3k
Reza A. Zoroofi Iran 24 717 1.8× 26 0.1× 416 1.2× 353 1.2× 296 1.1× 93 1.5k
Hans Lamecker Germany 20 522 1.3× 16 0.0× 271 0.8× 486 1.7× 136 0.5× 55 1.2k

Countries citing papers authored by Ewa Piętka

Since Specialization
Citations

This map shows the geographic impact of Ewa Piętka'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 Ewa Piętka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ewa Piętka more than expected).

Fields of papers citing papers by Ewa Piętka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ewa Piętka. 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 Ewa Piętka. The network helps show where Ewa Piętka may publish in the future.

Co-authorship network of co-authors of Ewa Piętka

This figure shows the co-authorship network connecting the top 25 collaborators of Ewa Piętka. A scholar is included among the top collaborators of Ewa Piętka 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 Ewa Piętka. Ewa Piętka 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.
Rydz, Joanna, Wanda Sikorska, Marta Musioł, et al.. (2024). Oligopeptide-based molecular labelling of (bio)degradable polyester biomaterials. International Journal of Biological Macromolecules. 268(Pt 1). 131561–131561. 4 indexed citations
2.
Li, Frédéric, Wacław M. Adamczyk, Tibor M. Szikszay, et al.. (2024). An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition. Scientific Data. 11(1). 1051–1051. 4 indexed citations
3.
Myśliwiec, Andrzej, et al.. (2024). Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data Patterns. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 32. 3565–3576. 6 indexed citations
4.
Czajkowska, Joanna, et al.. (2023). High-frequency ultrasound in anti-aging skin therapy monitoring. Scientific Reports. 13(1). 17799–17799. 13 indexed citations
5.
Myśliwiec, Andrzej, et al.. (2021). Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform. Sensors. 21(4). 1311–1311. 17 indexed citations
6.
Wijata, Agata M., et al.. (2020). Wound 3D Geometrical Feature Estimation Using Poisson Reconstruction. IEEE Access. 9. 7894–7907. 8 indexed citations
7.
Spińczyk, Dominik, Joanna Czajkowska, Marcin Rudzki, et al.. (2019). Supporting diagnostics and therapy planning for percutaneous ablation of liver and abdominal tumors and pre-clinical evaluation. Computerized Medical Imaging and Graphics. 78. 101664–101664. 3 indexed citations
8.
Czajkowska, Joanna, et al.. (2016). Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration. PLoS ONE. 11(7). e0159493–e0159493. 6 indexed citations
9.
Kawa, Jacek, et al.. (2015). Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis. Computerized Medical Imaging and Graphics. 46. 178–190. 26 indexed citations
10.
Piętka, Ewa, et al.. (2015). Granular computing in model based abdominal organs detection. Computerized Medical Imaging and Graphics. 46. 121–130. 14 indexed citations
11.
Badura, Paweł & Ewa Piętka. (2014). Soft computing approach to 3D lung nodule segmentation in CT. Computers in Biology and Medicine. 53. 230–243. 44 indexed citations
12.
Czajkowska, Joanna & Ewa Piętka. (2014). A new parametric model-based technique in bone tumour analysis. Computerized Medical Imaging and Graphics. 38(5). 315–325. 6 indexed citations
13.
Bugdol, Monika, Joanna Czajkowska, & Ewa Piętka. (2012). A novel model-based approach to left ventricle segmentation. Computing in Cardiology. 561–564. 2 indexed citations
14.
Kawa, Jacek, et al.. (2011). Quantitative tumour tissue measurements in subjects with high-grade gliomas. International Conference Mixed Design of Integrated Circuits and Systems. 56–61. 2 indexed citations
15.
Piętka, Ewa, Jacek Kawa, Dominik Spińczyk, et al.. (2009). Role of radiologists in CAD life-cycle. European Journal of Radiology. 78(2). 225–233. 15 indexed citations
16.
Piętka, Ewa, et al.. (2004). Computer-Assisted Bone Age Assessment: Graphical User Interface for Image Processing and Comparison. Journal of Digital Imaging. 17(3). 175–188. 25 indexed citations
17.
Cao, Fei, H. K. Huang, Ewa Piętka, & Vicente Gilsanz. (2000). Digital hand atlas and web-based bone age assessment: system design and implementation. Computerized Medical Imaging and Graphics. 24(5). 297–307. 52 indexed citations
18.
Piętka, Ewa. (1999). Database of diagnostic images in hospital integrated picture archiving and communication systems. SHILAP Revista de lepidopterología. 465–474. 1 indexed citations
19.
Piętka, Ewa & He Huang. (1992). Correction of aberration in image-intensifier systems. Computerized Medical Imaging and Graphics. 16(4). 253–258. 21 indexed citations
20.
Piętka, Ewa, Michael F. McNitt‐Gray, Min‐Liang Kuo, & H. K. Huang. (1991). Computer-assisted phalangeal analysis in skeletal age assessment. IEEE Transactions on Medical Imaging. 10(4). 616–620. 61 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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