A. Jonathan McLeod

30 papers receiving 401 citations

Peers

A. Jonathan McLeod
Comparison fields: 5 of 83
  • Gastroenterology 35
  • Computer Vision and Pattern Recognition 126
  • Radiology, Nuclear Medicine and Imaging 79
  • Pulmonary and Respiratory Medicine 103
  • Surgery 125
Replace Avi Ben-Cohen with:
Avi Ben-Cohen Israel
Shuzo Kanasaki Japan
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Duane D. Meixner United States
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Countries citing papers authored by A. Jonathan McLeod

Since Specialization
Citations

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

Fields of papers citing papers by A. Jonathan McLeod

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside A. Jonathan McLeod, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with A. Jonathan McLeod Line = papers co-authored together A. Jonathan McLeod links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 198494
2 201731
3 201831
4 201529
5 201428
6 202023
7 201521
8 201621
9 201318
10 201516
11 200015
12 201412
13 198410
14 20148
15 20128
16 20157
17
MAP-based Brain Tissue Segmentation using Manifold Learning and Hierarchical Max-Flow regularization
20147
18 20176
19 20165
20 20123

About A. Jonathan McLeod

A. Jonathan McLeod is a scholar working on Computer Vision and Pattern Recognition, Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging, Surgery and Biomedical Engineering, having authored 32 papers that have together received 415 indexed citations. Recurring topics across this work include Cardiac Valve Diseases and Treatments (8 papers), Medical Image Segmentation Techniques (6 papers), Advanced Vision and Imaging (5 papers), Ultrasound Imaging and Elastography (4 papers), Infective Endocarditis Diagnosis and Management (4 papers), Advanced MRI Techniques and Applications (4 papers), Robotics and Sensor-Based Localization (3 papers) and Cardiac Imaging and Diagnostics (3 papers). The work is most often cited by research in Gastroenterology (35 citations), Computer Vision and Pattern Recognition (126 citations), Radiology, Nuclear Medicine and Imaging (79 citations), Pulmonary and Respiratory Medicine (103 citations) and Surgery (125 citations). A. Jonathan McLeod has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Terry M. Peters, Ali Shirkhoda, J Zornoza, John S. H. Baxter, Elvis C. S. Chen, Martin Rajchl, Xióngbiāo Luó, Stephen E. Pautler, Michael Chu and John Moore. Their work appears in journals such as International Journal of Computer Assisted Radiology and Surgery, Radiology, Journal of Telemedicine and Telecare, Computerized Medical Imaging and Graphics and Journal of Ultrasound in Medicine.

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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