Jeny Rajan

2.8k total citations
84 papers, 1.9k citations indexed

About

Jeny Rajan is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Media Technology. According to data from OpenAlex, Jeny Rajan has authored 84 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Computer Vision and Pattern Recognition, 31 papers in Radiology, Nuclear Medicine and Imaging and 19 papers in Media Technology. Recurrent topics in Jeny Rajan's work include Image and Signal Denoising Methods (26 papers), Retinal Imaging and Analysis (15 papers) and Medical Image Segmentation Techniques (13 papers). Jeny Rajan is often cited by papers focused on Image and Signal Denoising Methods (26 papers), Retinal Imaging and Analysis (15 papers) and Medical Image Segmentation Techniques (13 papers). Jeny Rajan collaborates with scholars based in India, United States and Belgium. Jeny Rajan's co-authors include Jan Sijbers, S. J. Pawan, Chetan L. Srinidhi, P. Aparna, Abhishek Kothari, P. V. Sudeep, Chandrasekharan Kesavadas, Marleen Verhoye, P. Palanisamy and Jasjit S. Suri and has published in prestigious journals such as IEEE Transactions on Image Processing, Magnetic Resonance in Medicine and IEEE Access.

In The Last Decade

Jeny Rajan

82 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jeny Rajan India 26 921 884 355 273 260 84 1.9k
Dwarikanath Mahapatra Switzerland 23 905 1.0× 1.1k 1.2× 187 0.5× 352 1.3× 207 0.8× 87 1.8k
Gareth Funka-Lea United States 14 790 0.9× 1.7k 1.9× 159 0.4× 78 0.3× 324 1.2× 32 2.4k
Jian Yang China 31 1.5k 1.6× 1.5k 1.7× 237 0.7× 142 0.5× 945 3.6× 260 3.4k
Ismail Ben Ayed Canada 28 1.0k 1.1× 1.8k 2.0× 292 0.8× 66 0.2× 383 1.5× 136 3.1k
Georgy Gimel’farb New Zealand 25 1.5k 1.6× 1.0k 1.2× 104 0.3× 147 0.5× 360 1.4× 196 2.8k
J. Shin United States 7 1.0k 1.1× 848 1.0× 132 0.4× 71 0.3× 271 1.0× 7 2.5k
Weifang Zhu China 23 1.5k 1.6× 903 1.0× 144 0.4× 805 2.9× 688 2.6× 124 2.3k
Ozan Oktay United Kingdom 16 1.3k 1.4× 1.3k 1.5× 114 0.3× 81 0.3× 475 1.8× 29 2.7k
Xin Yang China 21 1.5k 1.6× 1.2k 1.4× 97 0.3× 154 0.6× 587 2.3× 86 3.1k
Jinzhu Yang China 23 621 0.7× 434 0.5× 104 0.3× 64 0.2× 121 0.5× 147 1.6k

Countries citing papers authored by Jeny Rajan

Since Specialization
Citations

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

Fields of papers citing papers by Jeny Rajan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jeny Rajan

This figure shows the co-authorship network connecting the top 25 collaborators of Jeny Rajan. A scholar is included among the top collaborators of Jeny Rajan 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 Jeny Rajan. Jeny Rajan 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.
Dayananda, P., et al.. (2024). Multilevel Multimodal Framework for Automatic Collateral Scoring in Brain Stroke. IEEE Access. 12. 33730–33748. 2 indexed citations
2.
Kannath, Santhosh Kumar, et al.. (2024). A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection. Engineering Applications of Artificial Intelligence. 133. 108192–108192. 3 indexed citations
3.
Kini, Jyoti, et al.. (2023). A deep learning based classifier framework for automated nuclear atypia scoring of breast carcinoma. Engineering Applications of Artificial Intelligence. 120. 105949–105949. 4 indexed citations
4.
Raghavendra, B. S., et al.. (2023). Machine learning techniques for periodontitis and dental caries detection: A narrative review. International Journal of Medical Informatics. 178. 105170–105170. 11 indexed citations
5.
Yudistira, Novanto, Muthu Subash Kavitha, Jeny Rajan, & Takio Kurita. (2023). Attention-effective multiple instance learning on weakly stem cell colony segmentation. Intelligent Systems with Applications. 17. 200187–200187. 3 indexed citations
6.
Kothari, Abhishek, et al.. (2023). A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images. IEEE Access. 11. 17241–17251. 18 indexed citations
7.
Rajan, Jeny, et al.. (2023). An automated deep learning pipeline for detecting user errors in spirometry test. Biomedical Signal Processing and Control. 90. 105845–105845. 3 indexed citations
8.
9.
Pawan, S. J., et al.. (2023). WideCaps: a wide attention-based capsule network for image classification. Machine Vision and Applications. 34(4). 2 indexed citations
10.
Kothari, Abhishek, et al.. (2020). Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images. Journal of Applied Biomedicine. 40(4). 1343–1358. 16 indexed citations
11.
Srinidhi, Chetan L., P. Aparna, & Jeny Rajan. (2017). Recent Advancements in Retinal Vessel Segmentation. Journal of Medical Systems. 41(4). 70–70. 91 indexed citations
12.
Sharma, Aditya, Ajay Gupta, Jeny Rajan, et al.. (2015). A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework. Current Atherosclerosis Reports. 17(9). 55–55. 30 indexed citations
13.
Veraart, Jelle, Jeny Rajan, Ronald Peeters, et al.. (2012). Comprehensive framework for accurate diffusion MRI parameter estimation. Magnetic Resonance in Medicine. 70(4). 972–984. 81 indexed citations
14.
Rajan, Jeny, Jelle Veraart, Johan Van Audekerke, Marleen Verhoye, & Jan Sijbers. (2012). Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images. Magnetic Resonance Imaging. 30(10). 1512–1518. 53 indexed citations
15.
Mai, Zhenhua, Jeny Rajan, Marleen Verhoye, & Jan Sijbers. (2011). Robust edge-directed interpolation of magnetic resonance images. Physics in Medicine and Biology. 56(22). 7287–7303. 14 indexed citations
16.
Rajan, Jeny, Ben Jeurissen, Marleen Verhoye, Johan Van Audekerke, & Jan Sijbers. (2011). Maximum likelihood estimation-based denoising of magnetic resonance images using restricted local neighborhoods. Physics in Medicine and Biology. 56(16). 5221–5234. 54 indexed citations
17.
Rajan, Jeny, et al.. (2010). Stability Analysis of Single Machine Infinite Bus Power System with TCSC Controller. 2(11). 354–359. 2 indexed citations
18.
Rajan, Jeny, et al.. (2010). Noise measurement from magnitude MRI using local estimates of variance and skewness. Physics in Medicine and Biology. 55(16). N441–N449. 72 indexed citations
19.
Sijbers, Jan, et al.. (2010). Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft‐tissue tumors in T1‐MRI images. Journal of Magnetic Resonance Imaging. 31(3). 680–689. 113 indexed citations
20.
Rajan, Jeny, K. Kannan, Chandrasekharan Kesavadas, & Bejoy Thomas. (2009). Focal Cortical Dysplasia (FCD) lesion analysis with complex diffusion approach. Computerized Medical Imaging and Graphics. 33(7). 553–558. 12 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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