F. Pernuš

2.0k total citations · 1 hit paper
31 papers, 1.5k citations indexed

About

F. Pernuš is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, F. Pernuš has authored 31 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 14 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Biomedical Engineering. Recurrent topics in F. Pernuš's work include Medical Image Segmentation Techniques (15 papers), Medical Imaging Techniques and Applications (11 papers) and Medical Imaging and Analysis (8 papers). F. Pernuš is often cited by papers focused on Medical Image Segmentation Techniques (15 papers), Medical Imaging Techniques and Applications (11 papers) and Medical Imaging and Analysis (8 papers). F. Pernuš collaborates with scholars based in Slovenia, Austria and United States. F. Pernuš's co-authors include B. Likar, Dejan Tomaževič, Primož Markelj, Max A. Viergever, T. Slivnik, Ida Eržen, Tomaž Vrtovec, Bulat Ibragimov, Žiga Špiclin and U Skalerič and has published in prestigious journals such as IEEE Transactions on Image Processing, International Journal of Radiation Oncology*Biology*Physics and IEEE Transactions on Medical Imaging.

In The Last Decade

F. Pernuš

30 papers receiving 1.4k citations

Hit Papers

A review of 3D/2D registration methods for image-guided i... 2010 2026 2015 2020 2010 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
F. Pernuš Slovenia 13 813 584 511 213 160 31 1.5k
Dejan Tomaževič Slovenia 16 752 0.9× 479 0.8× 537 1.1× 216 1.0× 169 1.1× 42 1.5k
B. Likar Slovenia 12 1.1k 1.4× 781 1.3× 487 1.0× 190 0.9× 150 0.9× 20 1.9k
Philipp G. Batchelor United Kingdom 3 872 1.1× 732 1.3× 363 0.7× 89 0.4× 175 1.1× 4 1.5k
Wolfgang Wein Germany 24 673 0.8× 727 1.2× 631 1.2× 320 1.5× 130 0.8× 70 1.8k
Jingfan Fan China 19 833 1.0× 605 1.0× 365 0.7× 177 0.8× 111 0.7× 124 1.5k
Isabelle E. Magnin France 24 692 0.9× 985 1.7× 424 0.8× 141 0.7× 44 0.3× 124 1.8k
Hans‐Peter Meinzer Germany 20 601 0.7× 287 0.5× 415 0.8× 329 1.5× 106 0.7× 132 1.4k
Danni Ai China 19 776 1.0× 501 0.9× 392 0.8× 245 1.2× 125 0.8× 154 1.4k
Peyton H. Bland United States 19 664 0.8× 1.1k 1.8× 338 0.7× 91 0.4× 75 0.5× 39 1.8k
Shun Miao United States 16 524 0.6× 456 0.8× 416 0.8× 182 0.9× 109 0.7× 41 1.2k

Countries citing papers authored by F. Pernuš

Since Specialization
Citations

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

Fields of papers citing papers by F. Pernuš

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of F. Pernuš

This figure shows the co-authorship network connecting the top 25 collaborators of F. Pernuš. A scholar is included among the top collaborators of F. Pernuš 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 F. Pernuš. F. Pernuš 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.
Špiclin, Žiga, et al.. (2017). Monoplane 3D–2D registration of cerebral angiograms based on multi-objective stratified optimization. Physics in Medicine and Biology. 62(24). 9377–9394. 1 indexed citations
2.
Vrtovec, Tomaž, B. Likar, & F. Pernuš. (2013). Manual and computerized measurement of coronal vertebral inclination on MRI images: A pilot study. Clinical Radiology. 68(8). 807–814. 2 indexed citations
3.
Štern, Darko, et al.. (2012). Quantitative vertebral morphometry based on parametric modeling of vertebral bodies in 3D. Osteoporosis International. 24(4). 1357–1368. 10 indexed citations
4.
Špiclin, Žiga, et al.. (2012). Groupwise Registration of Multimodal Images by an Efficient Joint Entropy Minimization Scheme. IEEE Transactions on Image Processing. 21(5). 2546–2558. 21 indexed citations
5.
Ibragimov, Bulat, B. Likar, F. Pernuš, & Tomaž Vrtovec. (2012). A Game-Theoretic Framework for Landmark-Based Image Segmentation. IEEE Transactions on Medical Imaging. 31(9). 1761–1776. 41 indexed citations
6.
Pawiro, Supriyanto Ardjo, Primož Markelj, F. Pernuš, et al.. (2011). Validation for 2D/3D registration I: A new gold standard data set. Medical Physics. 38(3). 1481–1490. 38 indexed citations
7.
Figl, Michael, Christoph Bloch, Christelle Gendrin, et al.. (2010). Efficient implementation of the rank correlation merit function for 2D/3D registration. Physics in Medicine and Biology. 55(19). N465–N471. 8 indexed citations
8.
Likar, B., et al.. (2007). A protocol for evaluation of similarity measures for non-rigid registration. Medical Image Analysis. 12(1). 42–54. 13 indexed citations
9.
Pernuš, F., et al.. (2006). A protocol for evaluation of similarity measures for rigid registration. IEEE Transactions on Medical Imaging. 25(6). 779–791. 61 indexed citations
10.
Vrtovec, Tomaž, B. Likar, & F. Pernuš. (2005). Spine-based coordinate system. PubMed. 8. 5120–5123. 3 indexed citations
11.
Tomaževič, Dejan, B. Likar, & F. Pernuš. (2005). 3-D/2-D registration by integrating 2-D information in 3-D. IEEE Transactions on Medical Imaging. 25(1). 17–27. 57 indexed citations
12.
Tomaževič, Dejan, B. Likar, T. Slivnik, & F. Pernuš. (2003). 3-D/2-D registration of CT and MR to X-ray images. IEEE Transactions on Medical Imaging. 22(11). 1407–1416. 123 indexed citations
13.
Fidler, Aleš, B. Likar, F. Pernuš, & U Skalerič. (2002). Impact of JPEG lossy image compression on quantitative digital subtraction radiography.. Dentomaxillofacial Radiology. 31(2). 106–112. 7 indexed citations
14.
Fidler, Aleš, B. Likar, F. Pernuš, & U Skalerič. (2002). Comparative evaluation of JPEG and JPEG2000 compression in quantitative digital subtraction radiography.. Dentomaxillofacial Radiology. 31(6). 379–384. 16 indexed citations
15.
Tomaževič, Dejan, B. Likar, & F. Pernuš. (2002). A comparison of retrospective shading correction techniques. 3. 564–567. 6 indexed citations
16.
Tomaževič, Dejan, B. Likar, & F. Pernuš. (2002). Comparative evaluation of retrospective shading correction methods. Journal of Microscopy. 208(3). 212–223. 71 indexed citations
17.
Likar, B., Max A. Viergever, & F. Pernuš. (2001). Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Transactions on Medical Imaging. 20(12). 1398–1410. 208 indexed citations
18.
Iglič, A., et al.. (1996). Determination of the femoral and pelvic geometrical parameters that are important for the hip joint contact stress: Differences between female and male. Pflügers Archiv - European Journal of Physiology. 431(S6). R207–R208. 7 indexed citations
19.
Bernard, Robert W., et al.. (1996). Contrast matching techniques for digital subtraction radiography: an objective evaluation.. PubMed. 294–8. 2 indexed citations
20.
Eržen, Ida, et al.. (1990). Muscle fibre types in the human vastus lateralis muscles: do symmetrical sites differ in their composition?. PubMed. 171(1). 55–63. 8 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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