G. Ravindran

487 total citations
40 papers, 361 citations indexed

About

G. Ravindran is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, G. Ravindran has authored 40 papers receiving a total of 361 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 13 papers in Radiology, Nuclear Medicine and Imaging and 11 papers in Artificial Intelligence. Recurrent topics in G. Ravindran's work include Medical Image Segmentation Techniques (16 papers), AI in cancer detection (11 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). G. Ravindran is often cited by papers focused on Medical Image Segmentation Techniques (16 papers), AI in cancer detection (11 papers) and Radiomics and Machine Learning in Medical Imaging (6 papers). G. Ravindran collaborates with scholars based in India. G. Ravindran's co-authors include S. Poonguzhali, R. Sivakumar, S. Lakshminarayanan, P. Pandiyan, Sumathi Poobal, Krishnan Ganapathy, R. Tamilselvi, Gurinder Mohan, Praveen Ravishankaran and Sivasubramanian Srinivasan and has published in prestigious journals such as SHILAP Revista de lepidopterología, BioMed Research International and Journal of Clinical Neuroscience.

In The Last Decade

G. Ravindran

30 papers receiving 293 citations

Peers

G. Ravindran
Comparison fields: 5 of 98
  • Computer Vision and Pattern Recognition 138
  • Artificial Intelligence 111
  • Radiology, Nuclear Medicine and Imaging 101
  • Media Technology 38
  • Molecular Biology 33
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Citations per field, relative to G. Ravindran
G. Ravindran · 1×
Citations per year, relative to G. Ravindran
G. Ravindran · 1×

Countries citing papers authored by G. Ravindran

Since Specialization
Citations

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

Fields of papers citing papers by G. Ravindran

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of G. Ravindran

This figure shows the co-authorship network connecting the top 25 collaborators of G. Ravindran. A scholar is included among the top collaborators of G. Ravindran 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 G. Ravindran. G. Ravindran 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
# Work Indexed citations
1 3
2 6
3 3
4
Inter-slice Reconstruction of MRI Image Using One Dimensional Signal Interpolation
3
5 2
6 31
7 0
8 5
9
Identification of Transient Visual Evoked Potential Latency Using Spectral Components
1
10
A complete automatic region growing method for segmentation of masses on ultrasound images
42
11 17
12 4
13 0
14 17
15 22
16 0
17 7
18
Chromosome Segmentation and Investigations using Generalized Gradient Vector Flow Active Contours
2
19 11
20 6

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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