Nigel John

6.8k citations
14 papers · 167 · h-index 7

Impact in

    • Brain Tumor Detection and Classification
    • Medical Image Segmentation Techniques
    • Advanced Neural Network Applications
    • Image Retrieval and Classification Techniques
    • Advanced Image and Video Retrieval Techniques
    • Digital Imaging for Blood Diseases

Papers in

Nigel John

14 papers receiving 155 citations

Peers

Nigel John
Comparison fields: 5 of 45
  • Neurology 55
  • Computer Vision and Pattern Recognition 97
  • Biophysics 12
  • Media Technology 14
  • Industrial and Manufacturing Engineering 15
Replace Yong Fan with:
Yong Fan China
D. Satyanarayana India
László Lefkovits Romania
Aimon Rahman Bangladesh
Heba M. Afify Egypt
Junaid Tariq Pakistan
Matteo Roffilli Italy
Menglin Jiang China
Manisha Saini India
Sameh Abd El-Ghany Saudi Arabia
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Citations per field
00.5×3.2×
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Citations per year

Countries citing papers authored by Nigel John

Since Specialization
Citations

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

Fields of papers citing papers by Nigel John

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 9 scholars most cited alongside Nigel John, 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 Nigel John Line = papers co-authored together Nigel John links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 200647
2 201431
3 199326
4 200716
5 201214
6 20078
7 20037
8 20154
9
Automatic ischemic stroke lesion segmentation using single MR modality and gravitational histogram optimization based brain segmentation
20133
10 20073
11 20023
12
Efficacy of Gabor-Wavelet versus statistical features for brain tumor classification in MRI: A comparative study
20132
13
A three-dimensional statistical model for image segmentation and its application to MR brain images
19992
14 19941

About Nigel John

Nigel John is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Neurology, Molecular Biology and Biophysics, having authored 14 papers that have together received 167 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (8 papers), Brain Tumor Detection and Classification (4 papers), Neural Networks and Applications (4 papers), Fuzzy Logic and Control Systems (2 papers), Spectroscopy and Chemometric Analyses (2 papers), Image Retrieval and Classification Techniques (2 papers), Cell Image Analysis Techniques (2 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Neurology (55 citations), Computer Vision and Pattern Recognition (97 citations), Biophysics (12 citations), Media Technology (14 citations) and Industrial and Manufacturing Engineering (15 citations). Nigel John has collaborated with scholars based in United States. Frequent co-authors include Mansur R. Kabuka, Akmal A. Younis, Mohamed Ibrahim, Clinton B. Wright, Nooshin Nabizadeh, Jia Xu, Miroslav Kubát, Ubbo Visser and Xiaohong Li. Their work appears in journals such as Journal of Digital Imaging, Image and Vision Computing, IEEE Transactions on Computers, Expert Systems with Applications and The Open Biomedical Engineering Journal.

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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