Manuel G. Penedo

3.0k total citations
121 papers, 1.9k citations indexed

About

Manuel G. Penedo is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Manuel G. Penedo has authored 121 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Radiology, Nuclear Medicine and Imaging, 48 papers in Ophthalmology and 44 papers in Computer Vision and Pattern Recognition. Recurrent topics in Manuel G. Penedo's work include Retinal Imaging and Analysis (49 papers), Glaucoma and retinal disorders (35 papers) and Retinal Diseases and Treatments (22 papers). Manuel G. Penedo is often cited by papers focused on Retinal Imaging and Analysis (49 papers), Glaucoma and retinal disorders (35 papers) and Retinal Diseases and Treatments (22 papers). Manuel G. Penedo collaborates with scholars based in Spain, United States and Portugal. Manuel G. Penedo's co-authors include Marcos Ortega, Jorge Novo, Marı́a J. Carreira, José Rouco, N. Barreira, A. Mosquera, D. Cabello, Beatriz Remeseiro, C. Mariño and Brais Cancela and has published in prestigious journals such as PLoS ONE, NeuroImage and Radiology.

In The Last Decade

Manuel G. Penedo

116 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manuel G. Penedo Spain 23 1.0k 751 594 273 232 121 1.9k
Jorge Novo Spain 22 1.1k 1.0× 605 0.8× 341 0.6× 293 1.1× 212 0.9× 142 1.7k
Marcos Ortega Spain 24 1.2k 1.2× 735 1.0× 461 0.8× 278 1.0× 221 1.0× 151 1.9k
Chua Kuang Chua Singapore 28 1.4k 1.3× 898 1.2× 786 1.3× 405 1.5× 616 2.7× 36 2.7k
Sulatha V. Bhandary India 27 2.0k 1.9× 1.6k 2.1× 1.1k 1.8× 197 0.7× 83 0.4× 71 2.5k
José Rouco Spain 18 899 0.9× 312 0.4× 524 0.9× 651 2.4× 214 0.9× 49 1.5k
Ruogu Fang United States 18 662 0.6× 307 0.4× 590 1.0× 273 1.0× 25 0.1× 71 1.6k
Dwarikanath Mahapatra Switzerland 23 905 0.9× 352 0.5× 1.1k 1.8× 464 1.7× 43 0.2× 87 1.8k
Beiji Zou China 24 802 0.8× 499 0.7× 1.2k 2.0× 324 1.2× 14 0.1× 189 2.1k
Bin Sheng China 24 611 0.6× 275 0.4× 1.0k 1.7× 348 1.3× 25 0.1× 114 2.1k
Joes Staal Netherlands 8 3.6k 3.5× 2.5k 3.3× 2.7k 4.6× 375 1.4× 44 0.2× 12 4.2k

Countries citing papers authored by Manuel G. Penedo

Since Specialization
Citations

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

Fields of papers citing papers by Manuel G. Penedo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manuel G. Penedo

This figure shows the co-authorship network connecting the top 25 collaborators of Manuel G. Penedo. A scholar is included among the top collaborators of Manuel G. Penedo 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 Manuel G. Penedo. Manuel G. Penedo 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.
Barreira, N., et al.. (2023). Generation of synthetic intermediate slices in 3D OCT cubes for improving pathology detection and monitoring. Computers in Biology and Medicine. 163. 107214–107214. 3 indexed citations
2.
Remeseiro, Beatriz, Javier Tarrío‐Saavedra, Mario Francisco‐Fernández, et al.. (2019). Automatic detection of defective crankshafts by image analysis and supervised classification. The International Journal of Advanced Manufacturing Technology. 105(9). 3761–3777. 14 indexed citations
3.
Moura, Joaquim de, et al.. (2019). Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model. Heliyon. 5(2). e01271–e01271. 25 indexed citations
4.
Moura, Joaquim de, et al.. (2018). Intraretinal fluid identification via enhanced maps using optical coherence tomography images. Biomedical Optics Express. 9(10). 4730–4730. 29 indexed citations
5.
Remeseiro, Beatriz, N. Barreira, Carlos García‐Resúa, et al.. (2016). iDEAS: A web-based system for dry eye assessment. Computer Methods and Programs in Biomedicine. 130. 186–197. 11 indexed citations
6.
Remeseiro, Beatriz, Verónica Bolón‐Canedo, Amparo Alonso‐Betanzos, & Manuel G. Penedo. (2015). Learning features on tear film lipid layer cla ssification. The European Symposium on Artificial Neural Networks. 2 indexed citations
7.
Bolón‐Canedo, Verónica, et al.. (2015). On the use of machine learning techniques for the analysis of spontaneous reactions in automated hearing assessment. The European Symposium on Artificial Neural Networks. 1 indexed citations
8.
Remeseiro, Beatriz, A. Mosquera, & Manuel G. Penedo. (2015). CASDES: A Computer-Aided System to Support Dry Eye Diagnosis Based on Tear Film Maps. IEEE Journal of Biomedical and Health Informatics. 20(3). 936–943. 16 indexed citations
9.
Brea, Luisa Sánchez, N. Barreira, Manuel G. Penedo, & Brais Cancela. (2015). Automatic identification of vessel crossovers in retinal images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9445. 94451G–94451G. 1 indexed citations
10.
Cancela, Brais, et al.. (2014). Unsupervised Trajectory Modelling Using Temporal Information via Minimal Paths. 1. 2553–2560. 14 indexed citations
11.
Barreira, N., et al.. (2014). Automatic Segmentation of the Mandible in Cone-Beam Computer Tomography Images. 467–468. 3 indexed citations
12.
Ramos, Lucía, N. Barreira, A. Mosquera, et al.. (2012). Adaptive parameter computation for the automatic measure of the Tear Break-Up Time.. 243. 1370–1379. 3 indexed citations
13.
Novo, Jorge, Manuel G. Penedo, & José Sántos. (2010). Evolutionary multiobjective optimization of Topological Active Nets. Pattern Recognition Letters. 31(13). 1781–1794. 6 indexed citations
14.
Ortega, Marcos, Manuel G. Penedo, José Rouco, N. Barreira, & Marı́a J. Carreira. (2009). Retinal Verification Using a Feature Points-Based Biometric Pattern. EURASIP Journal on Advances in Signal Processing. 2009(1). 64 indexed citations
15.
Mariño, C., et al.. (2008). Automated three stage red lesions detection in digital color fundus images. WSEAS Transactions on Computers archive. 7(4). 207–215. 9 indexed citations
16.
Mariño, C., et al.. (2008). CREST LINES AND CORRELATION FILTER BASED LOCATION OF THE MACULA IN DIGITAL RETINAL IMAGES. 521–527. 1 indexed citations
17.
Mariño, C., et al.. (2007). Macula precise localization using digital retinal angiographies. Annual Conference on Computers. 3(1). 43–50. 10 indexed citations
18.
Ortega, Marcos, et al.. (2006). Biometric authentication using digital retinal images. 422–427. 34 indexed citations
19.
Pascau, Javier, Manuel G. Penedo, Juan Domingo Gispert, et al.. (2002). Cuantificación en estudios PET: métodos y aplicaciones. 96(1). 13. 2 indexed citations
20.
Carreira, Marı́a J., D. Cabello, Manuel G. Penedo, & A. Mosquera. (1998). Computer‐aided diagnoses: Automatic detection of lung nodules. Medical Physics. 25(10). 1998–2006. 35 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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