Michalis A. Savelonas

943 total citations
59 papers, 694 citations indexed

About

Michalis A. Savelonas is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Michalis A. Savelonas has authored 59 papers receiving a total of 694 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Computer Vision and Pattern Recognition, 21 papers in Artificial Intelligence and 14 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Michalis A. Savelonas's work include Medical Image Segmentation Techniques (18 papers), AI in cancer detection (18 papers) and Cell Image Analysis Techniques (10 papers). Michalis A. Savelonas is often cited by papers focused on Medical Image Segmentation Techniques (18 papers), AI in cancer detection (18 papers) and Cell Image Analysis Techniques (10 papers). Michalis A. Savelonas collaborates with scholars based in Greece, United States and Norway. Michalis A. Savelonas's co-authors include Dimitris Maroulis, Dimitris K. Iakovidis, Nikos Dimitropoulos, Ioannis Pratikakis, Konstantinos Sfikas, S.A. Karkanis, Spiros Chountasis, Ioannis Legakis, Evaggelos Spyrou and Anestis Koutsoudis and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, Pattern Recognition and IEEE Transactions on Cybernetics.

In The Last Decade

Michalis A. Savelonas

55 papers receiving 663 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michalis A. Savelonas Greece 17 333 278 240 132 56 59 694
Basil Mustafa Switzerland 5 346 1.0× 393 1.4× 247 1.0× 53 0.4× 9 0.2× 6 810
Kersten Petersen Denmark 8 257 0.8× 404 1.5× 449 1.9× 15 0.1× 11 0.2× 13 951
M.P. Ramo United Kingdom 8 200 0.6× 24 0.1× 92 0.4× 114 0.9× 16 0.3× 14 524
W.A. Sandham United Kingdom 12 295 0.9× 148 0.5× 49 0.2× 84 0.6× 10 0.2× 41 667
Stephen Chang Singapore 9 318 1.0× 117 0.4× 129 0.5× 20 0.2× 13 0.2× 20 577
Jianbo Jiao United Kingdom 17 780 2.3× 231 0.8× 94 0.4× 5 0.0× 35 0.6× 58 1.0k
Jing Zheng China 12 131 0.4× 262 0.9× 165 0.7× 14 0.1× 35 0.6× 36 606
Haoyuan Chen China 14 253 0.8× 506 1.8× 327 1.4× 5 0.0× 11 0.2× 25 813
Irina Voiculescu United Kingdom 11 187 0.6× 104 0.4× 158 0.7× 6 0.0× 86 1.5× 43 536
Anfei Fan United States 3 585 1.8× 132 0.5× 160 0.7× 4 0.0× 44 0.8× 6 699

Countries citing papers authored by Michalis A. Savelonas

Since Specialization
Citations

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

Fields of papers citing papers by Michalis A. Savelonas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michalis A. Savelonas. 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 Michalis A. Savelonas. The network helps show where Michalis A. Savelonas may publish in the future.

Co-authorship network of co-authors of Michalis A. Savelonas

This figure shows the co-authorship network connecting the top 25 collaborators of Michalis A. Savelonas. A scholar is included among the top collaborators of Michalis A. Savelonas 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 Michalis A. Savelonas. Michalis A. Savelonas 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.
Savelonas, Michalis A.. (2025). An Overview of AI-Guided Thyroid Ultrasound Image Segmentation and Classification for Nodule Assessment. Big Data and Cognitive Computing. 9(10). 255–255.
2.
Savelonas, Michalis A., et al.. (2024). Self-attention-driven retrieval of chest CT images for COVID-19 assessment. Biomedical Physics & Engineering Express. 10(2). 25013–25013.
3.
Savelonas, Michalis A., et al.. (2024). TransLevelSet: Integrating vision transformers with level-sets for medical image segmentation. Neurocomputing. 599. 128077–128077. 3 indexed citations
4.
Savelonas, Michalis A., et al.. (2023). SUShe: simple unsupervised shadow removal. Multimedia Tools and Applications. 83(7). 19517–19539. 2 indexed citations
5.
Savelonas, Michalis A., et al.. (2022). Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey. Remote Sensing. 14(23). 6017–6017. 17 indexed citations
6.
Savelonas, Michalis A., et al.. (2021). Vertebrae, IVD and spinal canal boundary extraction on MRI, utilizing CT-trained active shape models. International Journal of Computer Assisted Radiology and Surgery. 16(12). 2201–2214. 4 indexed citations
7.
Savelonas, Michalis A., et al.. (2017). Exploiting Unbroken Surface Congruity for the Acceleration of Fragment Reassembly. Eurographics. 1 indexed citations
8.
Tsochatzidis, Lazaros, Konstantinos Zagoris, Michalis A. Savelonas, & Ioannis Pratikakis. (2014). SVM-based CBIR of breast masses on mammograms. International Conference on Artificial Intelligence. 26–30. 3 indexed citations
9.
Savelonas, Michalis A., et al.. (2014). Self-parameterized active contours based on regional edge structure for medical image segmentation. SpringerPlus. 3(1). 424–424. 8 indexed citations
10.
Savelonas, Michalis A., et al.. (2013). Self-adjusted active contours using multi-directional texture cues. 3026–3030. 1 indexed citations
11.
Savelonas, Michalis A., et al.. (2012). Entropy-based spatially-varying adjustment of active contour parameters. 2565–2568. 9 indexed citations
12.
Savelonas, Michalis A., et al.. (2011). An automatically initialized level-set approach for the segmentation of proteomics images. 2869. 1–4. 1 indexed citations
13.
Savelonas, Michalis A., et al.. (2010). A level set approach for proteomics image analysis. European Signal Processing Conference. 1229–1233. 2 indexed citations
14.
Savelonas, Michalis A., et al.. (2009). Segmentation of two-dimensional gel electrophoresis images containing overlapping spots. 9. 1–4. 5 indexed citations
15.
Iakovidis, Dimitris K., et al.. (2009). Robust model-based detection of the lung field boundaries in portable chest radiographs supported by selective thresholding. Measurement Science and Technology. 20(10). 104019–104019. 16 indexed citations
16.
Savelonas, Michalis A., et al.. (2009). A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features. Computer Methods and Programs in Biomedicine. 96(1). 25–32. 28 indexed citations
17.
Savelonas, Michalis A., Dimitris K. Iakovidis, Ioannis Legakis, & Dimitris Maroulis. (2008). Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Images. PubMed. 13(4). 519–527. 49 indexed citations
18.
Savelonas, Michalis A., Dimitris Maroulis, Dimitris K. Iakovidis, & Nikos Dimitropoulos. (2008). Computer-Aided Malignancy Risk Assessment of Nodules in Thyroid US Images Utilizing Boundary Descriptors. 84. 157–160. 12 indexed citations
19.
Savelonas, Michalis A., Dimitris K. Iakovidis, & Dimitris Maroulis. (2008). LBP-guided active contours. Pattern Recognition Letters. 29(9). 1404–1415. 25 indexed citations
20.
Maroulis, Dimitris, et al.. (2007). Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images. IEEE Transactions on Information Technology in Biomedicine. 11(5). 537–543. 71 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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