Said Pertuz

1.4k total citations · 1 hit paper
47 papers, 889 citations indexed

About

Said Pertuz is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Said Pertuz has authored 47 papers receiving a total of 889 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 16 papers in Radiology, Nuclear Medicine and Imaging and 15 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Said Pertuz's work include AI in cancer detection (17 papers), Digital Radiography and Breast Imaging (14 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Said Pertuz is often cited by papers focused on AI in cancer detection (17 papers), Digital Radiography and Breast Imaging (14 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Said Pertuz collaborates with scholars based in Colombia, Finland and Spain. Said Pertuz's co-authors include Domènec Puig, Miguel Ángel García, Andrea Fusiello, Irina Rinta‐Kiikka, Otso Arponen, Joni‐Kristian Kämäräinen, Antti Tolonen, Despina Kontos, Emily F. Conant and Jiřı́ Matas and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Radiology.

In The Last Decade

Said Pertuz

43 papers receiving 841 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Analysis of focus measure operators for shape-from-focus

2012 457 citations Said Pertuz, Domènec Puig et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Said Pertuz Colombia	11	458	384	196	118	118	47	889
Faliu Yi South Korea	17	229 0.5×	347 0.9×	333 1.7×	117 1.0×	144 1.2×	42	850
Yuri Murakami Japan	16	153 0.3×	270 0.7×	230 1.2×	54 0.5×	58 0.5×	65	622
Boris Escalante‐Ramírez Mexico	14	183 0.4×	302 0.8×	65 0.3×	118 1.0×	87 0.7×	106	726
Yoichi Miyake Japan	19	161 0.4×	568 1.5×	535 2.7×	204 1.7×	218 1.8×	143	1.4k
Elli Angelopoulou Germany	18	305 0.7×	1.1k 2.9×	160 0.8×	373 3.2×	73 0.6×	59	1.5k
Suyeon Choi South Korea	17	721 1.6×	284 0.7×	550 2.8×	49 0.4×	97 0.8×	43	1.3k
J. Serra France	17	167 0.4×	642 1.7×	51 0.3×	42 0.4×	40 0.3×	58	1.1k
Sejung Yang South Korea	15	77 0.2×	168 0.4×	51 0.3×	95 0.8×	120 1.0×	71	722
Haishu Tan China	16	496 1.1×	492 1.3×	169 0.9×	87 0.7×	169 1.4×	76	955
Esteban Vera Chile	15	256 0.6×	390 1.0×	250 1.3×	60 0.5×	289 2.4×	70	1.1k

Countries citing papers authored by Said Pertuz

Since Specialization

Citations

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

Fields of papers citing papers by Said Pertuz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Said Pertuz

This figure shows the co-authorship network connecting the top 25 collaborators of Said Pertuz. A scholar is included among the top collaborators of Said Pertuz 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 Said Pertuz. Said Pertuz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Pertuz, Said, et al.. (2025). Breast cancer detection from ultrasound computed tomography imaging using radiomic analysis: in silico trial. Medical Physics. 52(4). 2465–2474.

Pertuz, Said, et al.. (2024). Deep Learning-Based Pulmonary Arterial Segmentation in Computed Tomography Images. 1–5.

Pertuz, Said, et al.. (2023). Saliency of breast lesions in breast cancer detection using artificial intelligence. Scientific Reports. 13(1). 20545–20545. 11 indexed citations

Pertuz, Said, et al.. (2023). MOOC-Based Flipped Classroom for On-Campus Teaching in Undergraduate Engineering Courses. IEEE Transactions on Education. 66(5). 468–478. 7 indexed citations

Miranda, David A., et al.. (2023). An in silico study on the detectability of field cancerization through parenchymal analysis of digital mammograms. Medical Physics. 50(10). 6379–6389. 1 indexed citations

Pertuz, Said, et al.. (2022). Course Quality Assessment in Post-pandemic Higher Education. 120–125. 4 indexed citations

Miranda, David A., et al.. (2021). Algorithms and methods for computerized analysis of mammography images in breast cancer risk assessment. Computer Methods and Programs in Biomedicine. 212. 106443–106443. 5 indexed citations

Tolonen, Antti, et al.. (2021). Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review. European Journal of Radiology. 145. 109943–109943. 85 indexed citations

Pertuz, Said, et al.. (2020). A convolutional oculomotor representation to model parkinsonian fixational patterns from magnified videos. Pattern Analysis and Applications. 24(2). 445–457. 5 indexed citations

10.

Pertuz, Said, et al.. (2020). Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps. SHILAP Revista de lepidopterología. 23(48). 143–161. 1 indexed citations

11.

Arponen, Otso, et al.. (2020). A Comparison of Regions of Interest in Parenchymal Analysis for Breast Cancer Risk Assessment. PubMed. 2020. 1136–1139. 3 indexed citations

12.

Miranda, David A. & Said Pertuz. (2019). Field cancerization in the understanding of parenchymal analysis of mammograms for breast cancer risk assessment. Medical Hypotheses. 136. 109511–109511. 3 indexed citations

13.

Arponen, Otso, et al.. (2019). Morphological Area Gradient: System-independent Dense Tissue Segmentation in Mammography Images. PubMed. 2019. 4855–4858. 6 indexed citations

14.

Pertuz, Said, et al.. (2019). Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample. European Journal of Radiology. 121. 108710–108710. 10 indexed citations

15.

Pertuz, Said, et al.. (2018). Focus model for metric depth estimation in standard plenoptic cameras. ISPRS Journal of Photogrammetry and Remote Sensing. 144. 38–47. 19 indexed citations

16.

Chen, Lin, Shonket Ray, Brad M. Keller, et al.. (2016). The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006. Radiology. 280(3). 693–700. 1 indexed citations

17.

Santamaria, Juan Carlos, et al.. (2016). A method for improving depth estimation in light field images. 1–7. 1 indexed citations

18.

Pertuz, Said, Elizabeth S. McDonald, Susan P. Weinstein, Emily F. Conant, & Despina Kontos. (2015). Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging. Radiology. 279(1). 65–74. 34 indexed citations

19.

Pertuz, Said, Domènec Puig, & Miguel Ángel García. (2013). Reliability measure for shape-from-focus. Image and Vision Computing. 31(10). 725–734. 26 indexed citations

20.

Pertuz, Said, Domènec Puig, Miguel Ángel García, & Andrea Fusiello. (2012). Generation of All-in-Focus Images by Noise-Robust Selective Fusion of Limited Depth-of-Field Images. IEEE Transactions on Image Processing. 22(3). 1242–1251. 74 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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