F. Segovia

3.5k total citations
82 papers, 2.0k citations indexed

About

F. Segovia is a scholar working on Computer Vision and Pattern Recognition, Neurology and Artificial Intelligence. According to data from OpenAlex, F. Segovia has authored 82 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Computer Vision and Pattern Recognition, 24 papers in Neurology and 18 papers in Artificial Intelligence. Recurrent topics in F. Segovia's work include Medical Image Segmentation Techniques (23 papers), Brain Tumor Detection and Classification (23 papers) and Image Retrieval and Classification Techniques (19 papers). F. Segovia is often cited by papers focused on Medical Image Segmentation Techniques (23 papers), Brain Tumor Detection and Classification (23 papers) and Image Retrieval and Classification Techniques (19 papers). F. Segovia collaborates with scholars based in Spain, United Kingdom and Belgium. F. Segovia's co-authors include J. M. Górriz, Javier Ramı́rez, D. Salas-González, I. Álvarez, M. López, R. Chaves, Carlos G. Puntonet, Pablo Padilla, A. Brahim and L. Khedher and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and IEEE Access.

In The Last Decade

F. Segovia

79 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
F. Segovia Spain 26 668 621 461 459 435 82 2.0k
I. Álvarez Spain 30 714 1.1× 752 1.2× 431 0.9× 515 1.1× 422 1.0× 75 2.2k
M. López Spain 22 591 0.9× 562 0.9× 367 0.8× 387 0.8× 334 0.8× 42 1.7k
D. Salas-González Spain 31 872 1.3× 983 1.6× 398 0.9× 656 1.4× 424 1.0× 92 2.5k
Iman Beheshti Canada 22 538 0.8× 257 0.4× 635 1.4× 275 0.6× 525 1.2× 63 1.7k
Ricardo Guerrero United Kingdom 18 427 0.6× 332 0.5× 377 0.8× 372 0.8× 386 0.9× 40 1.7k
Kim‐Han Thung United States 22 411 0.6× 552 0.9× 352 0.8× 597 1.3× 344 0.8× 57 1.9k
Manhua Liu China 31 1.2k 1.8× 999 1.6× 459 1.0× 889 1.9× 785 1.8× 103 3.5k
Francisco J. Martínez-Murcia Spain 21 314 0.5× 238 0.4× 304 0.7× 264 0.6× 183 0.4× 62 1.3k
Rémi Cuingnet France 12 579 0.9× 498 0.8× 425 0.9× 278 0.6× 669 1.5× 23 1.6k

Countries citing papers authored by F. Segovia

Since Specialization
Citations

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

Fields of papers citing papers by F. Segovia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of F. Segovia

This figure shows the co-authorship network connecting the top 25 collaborators of F. Segovia. A scholar is included among the top collaborators of F. Segovia 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 F. Segovia. F. Segovia 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.
Górriz, J. M., Javier Ramı́rez, F. Segovia, et al.. (2025). Statistical agnostic regression: A machine learning method to validate regression models. Journal of Advanced Research. 80. 503–533. 1 indexed citations
2.
Martínez-Murcia, Francisco J., Juan E. Arco, C. Jiménez-Mesa, et al.. (2024). Bridging Imaging and Clinical Scores in Parkinson’s Progression via Multimodal Self-Supervised Deep Learning. International Journal of Neural Systems. 34(8). 2450043–2450043. 7 indexed citations
3.
Segovia, F., Javier Ramı́rez, D. Salas-González, et al.. (2023). Connected system for monitoring electrical power transformers using thermal imaging. Integrated Computer-Aided Engineering. 30(4). 353–368. 3 indexed citations
4.
Górriz, J. M., C. Jiménez-Mesa, F. Segovia, et al.. (2021). Statistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities. arXiv (Cornell University). 13 indexed citations
5.
Castillo-Barnés, Diego, Li Su, Javier Ramı́rez, et al.. (2020). Autosomal dominantly inherited alzheimer disease: Analysis of genetic subgroups by machine learning. Information Fusion. 58. 153–167. 15 indexed citations
6.
Segovia, F., et al.. (2018). Using CT Data to Improve the Quantitative Analysis of 18F-FBB PET Neuroimages. Frontiers in Aging Neuroscience. 10. 158–158. 4 indexed citations
7.
Castillo-Barnés, Diego, Francisco J. Martínez-Murcia, F. Segovia, et al.. (2017). A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI. Frontiers in Neuroinformatics. 11. 66–66. 3 indexed citations
8.
Martínez-Murcia, Francisco J., J. M. Górriz, Javier Ramı́rez, et al.. (2017). Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modeling: Application to Neurodegenerative Diseases. Frontiers in Neuroinformatics. 11. 65–65. 11 indexed citations
9.
Salas-González, D., et al.. (2016). An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease. Current Alzheimer Research. 13(7). 838–844. 6 indexed citations
10.
Segovia, F., I. Álvarez, J. M. Górriz, et al.. (2015). Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks. Frontiers in Computational Neuroscience. 9. 137–137. 23 indexed citations
11.
12.
Segovia, F., I. Álvarez, D. Salas-González, et al.. (2014). PETRA: Multivariate analyses for neuroimaging data. Open Repository and Bibliography (University of Liège). 1302–1312. 2 indexed citations
13.
Meulemans, Thierry, et al.. (2014). Visual neglect: Is there a relationship between impaired spatial working memory and re-cancellation?. Experimental Brain Research. 232(10). 3333–3343. 19 indexed citations
14.
Segovia, F., Christine Bastin, Éric Salmon, et al.. (2014). Combining PET Images and Neuropsychological Test Data for Automatic Diagnosis of Alzheimer's Disease. PLoS ONE. 9(2). e88687–e88687. 29 indexed citations
15.
Segovia, F., et al.. (2012). Automatic differentiation between controls and Parkinson's disease DaTSCAN images using a Partial Least Squares scheme and the Fisher Discriminant Ratio.. 2241–2250. 3 indexed citations
16.
Ramı́rez, Javier, J. M. Górriz, F. Segovia, et al.. (2010). Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification. Neuroscience Letters. 472(2). 99–103. 98 indexed citations
17.
Padilla, Pablo, J. M. Górriz, Javier Ramı́rez, et al.. (2010). Analysis of SPECT brain images for the diagnosis of Alzheimer's disease based on NMF for feature extraction. Neuroscience Letters. 479(3). 192–196. 21 indexed citations
18.
Salas-González, D., J. M. Górriz, Javier Ramı́rez, et al.. (2010). Feature selection using factor analysis for Alzheimer's diagnosis using PET images. Medical Physics. 37(11). 6084–6095. 60 indexed citations
19.
Segovia, F., J. M. Górriz, Javier Ramı́rez, et al.. (2010). Classification of functional brain images using a GMM-based multi-variate approach. Neuroscience Letters. 474(1). 58–62. 33 indexed citations
20.
López, M., Javier Ramı́rez, J. M. Górriz, et al.. (2009). SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA. Neuroscience Letters. 464(3). 233–238. 97 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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