Kumaradevan Punithakumar

2.5k total citations
130 papers, 1.5k citations indexed

About

Kumaradevan Punithakumar is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Kumaradevan Punithakumar has authored 130 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Radiology, Nuclear Medicine and Imaging, 43 papers in Computer Vision and Pattern Recognition and 33 papers in Biomedical Engineering. Recurrent topics in Kumaradevan Punithakumar's work include Medical Image Segmentation Techniques (35 papers), Advanced MRI Techniques and Applications (22 papers) and Dental Radiography and Imaging (18 papers). Kumaradevan Punithakumar is often cited by papers focused on Medical Image Segmentation Techniques (35 papers), Advanced MRI Techniques and Applications (22 papers) and Dental Radiography and Imaging (18 papers). Kumaradevan Punithakumar collaborates with scholars based in Canada, China and Spain. Kumaradevan Punithakumar's co-authors include Michelle Noga, Ismail Ben Ayed, T. Kirubarajan, Shuo Li, Abhijit Sinha, Pierre Boulanger, Jacob L. Jaremko, Abhilash Rakkunedeth Hareendranathan, Ian Ross and Ali Islam and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Kumaradevan Punithakumar

118 papers receiving 1.4k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Kumaradevan Punithakumar 489 447 389 305 255 130 1.5k
Adrian Barbu 399 0.8× 292 0.7× 810 2.1× 300 1.0× 162 0.6× 73 1.5k
Michał Strzelecki 1.1k 2.3× 359 0.8× 428 1.1× 376 1.2× 120 0.5× 106 2.4k
Kuo‐Sheng Cheng 190 0.4× 223 0.5× 253 0.7× 318 1.0× 159 0.6× 97 1.8k
Martin Urschler 789 1.6× 652 1.5× 890 2.3× 476 1.6× 123 0.5× 87 2.4k
Tim McInerney 661 1.4× 419 0.9× 2.0k 5.2× 421 1.4× 116 0.5× 25 2.8k
Hong Song 444 0.9× 268 0.6× 562 1.4× 315 1.0× 49 0.2× 182 1.4k
J. Shin 1.0k 2.1× 1.1k 2.5× 848 2.2× 271 0.9× 104 0.4× 7 2.5k
Danni Ai 501 1.0× 185 0.4× 776 2.0× 392 1.3× 64 0.3× 154 1.4k
Gemma Piella 576 1.2× 345 0.8× 1.1k 2.8× 322 1.1× 422 1.7× 122 2.7k
Tobias Heimann 548 1.1× 276 0.6× 988 2.5× 557 1.8× 145 0.6× 44 2.0k

Countries citing papers authored by Kumaradevan Punithakumar

Since Specialization
Citations

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

Fields of papers citing papers by Kumaradevan Punithakumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kumaradevan Punithakumar

This figure shows the co-authorship network connecting the top 25 collaborators of Kumaradevan Punithakumar. A scholar is included among the top collaborators of Kumaradevan Punithakumar 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 Kumaradevan Punithakumar. Kumaradevan Punithakumar 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.
Punithakumar, Kumaradevan, et al.. (2025). Optimizing CBCT analysis of the Adenoid region: A deep learning approach. 1(1). 100013–100013.
2.
Punithakumar, Kumaradevan, Rui Zheng, Michelle Noga, et al.. (2025). Speckle Noise Reduction Techniques in Ultrasound Imaging: A comprehensive review of the last two decades (2005–2024). Computer Methods and Programs in Biomedicine. 274. 109150–109150.
3.
Punithakumar, Kumaradevan, Michelle Noga, Rui Zheng, et al.. (2024). A Lightweight Ultrasound Image Denoiser Using Parallel Attention Modules and Capsule Generative Adversarial Network. Informatics in Medicine Unlocked. 50. 101569–101569. 2 indexed citations
4.
Punithakumar, Kumaradevan, et al.. (2024). Force Controlled Operation of Robotic Arm for Echocardiography Scanning. 108–112.
5.
Chen, Hongbo, Sheng Song, Bin Zhang, et al.. (2024). Neural implicit surface reconstruction of freehand 3D ultrasound volume with geometric constraints. Medical Image Analysis. 98. 103305–103305. 9 indexed citations
6.
Noga, Michelle, et al.. (2024). Denoising Echocardiography with an Improved Diffusion Model. PubMed. 2024. 1–4. 1 indexed citations
7.
Punithakumar, Kumaradevan, Rui Zheng, Michelle Noga, et al.. (2024). Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network. IEEE Access. 12. 168607–168625. 2 indexed citations
8.
Punithakumar, Kumaradevan, Michelle Noga, Pierre Boulanger, & Harald Becher. (2023). Robot-Assisted Multiview Fusion of Three-Dimensional Echocardiography: A Phantom Study. 561–566. 1 indexed citations
9.
Qin, Xuebin, Masood Dehghan, Dornoosh Zonoobi, et al.. (2023). ACU2E-Net: A novel predict–refine attention network for segmentation of soft-tissue structures in ultrasound images. Computers in Biology and Medicine. 157. 106792–106792. 4 indexed citations
10.
Noga, Michelle, et al.. (2023). Benefit of stereoscopic volume rendering for the identification of pediatric pulmonary vein stenosis from CT angiography. SHILAP Revista de lepidopterología. 2(3). e0000215–e0000215.
11.
Zonoobi, Dornoosh, Russell Greiner, Jacob L. Jaremko, et al.. (2021). Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19. Informatics in Medicine Unlocked. 25. 100687–100687. 26 indexed citations
12.
Punithakumar, Kumaradevan, et al.. (2021). The implications of two outlet boundary conditions on blood flow simulations in normal aorta of pediatric subjects. Theoretical and Computational Fluid Dynamics. 35(3). 419–436. 3 indexed citations
13.
14.
Li, Mengxun, Xiangyang Xu, Kumaradevan Punithakumar, et al.. (2020). Automated integration of facial and intra-oral images of anterior teeth. Computers in Biology and Medicine. 122. 103794–103794. 11 indexed citations
15.
Punithakumar, Kumaradevan, Ismail Ben Ayed, Aashish Goela, et al.. (2020). 3D Motion Estimation of Left Ventricular Dynamics Using MRI and Track-to-Track Fusion. IEEE Journal of Translational Engineering in Health and Medicine. 8. 1–9. 2 indexed citations
16.
17.
Boulanger, Pierre, et al.. (2018). Tracking tumor boundary using point correspondence for adaptive radio therapy. Computer Methods and Programs in Biomedicine. 165. 187–195. 5 indexed citations
18.
Tharmarasa, Ratnasingham, et al.. (2012). Distributed Tracking with a PHD Filter using Efficient Measurement Encoding.. 7. 114–130. 2 indexed citations
19.
Punithakumar, Kumaradevan, Ismail Ben Ayed, Ali Islam, Aashish Goela, & Shuo Li. (2012). Regional Heart Motion Abnormality Detection via Multiview Fusion. Lecture notes in computer science. 15(Pt 2). 527–534. 2 indexed citations
20.
Ayed, Ismail Ben, Kumaradevan Punithakumar, Gregory J. Garvin, Walter Romano, & Shuo Li. (2011). Graph Cuts with Invariant Object-Interaction Priors: Application to Intervertebral Disc Segmentation. Lecture notes in computer science. 22. 221–232. 40 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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