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Journal articles on the topic 'Medical visualization'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Eljadid, Mahmoud G., and Amar Aggoun. "Medical 3D Integral Images Visualization in True Space." Lecture Notes on Software Engineering 4, no. 2 (2016): 87–90. http://dx.doi.org/10.7763/lnse.2016.v4.229.

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2

SEKI, Yasuhiro. "Visualization on Medical Equipment." Journal of the Visualization Society of Japan 22, no. 85 (2002): 92–96. http://dx.doi.org/10.3154/jvs.22.92.

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3

Faao, Sherman Gorbis,, and Richard C. Hallgren. "Visualization technology in medical education." Journal of the American Osteopathic Association 99, no. 4 (1999): 211. http://dx.doi.org/10.7556/jaoa.1999.99.4.211.

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4

HORITA, Katsuhei, Kazunori KAJIWARA, Kunio KONDO, Yasuaki ARAI, and Choichiro KIDO. "Flow Visualization on Medical Imaging." Journal of the Visualization Society of Japan 11, no. 40 (1991): 10–15. http://dx.doi.org/10.3154/jvs.11.10.

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5

Maupu, D., M. H. Van Horn, S. Weeks, and E. Bullitt. "3D stereo interactive medical visualization." IEEE Computer Graphics and Applications 25, no. 5 (2005): 67–71. http://dx.doi.org/10.1109/mcg.2005.94.

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6

Kirmizibayrak, Can, Nadezhda Radeva, Mike Wakid, John Philbeck, John Sibert, and James Hahn. "Evaluation of Gesture Based Interfaces for Medical Volume Visualization Tasks." International Journal of Virtual Reality 11, no. 2 (2012): 1–13. http://dx.doi.org/10.20870/ijvr.2012.11.2.2839.

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Interactive systems are increasingly used in medical applications with the widespread availability of various imaging modalities. Gesture-based interfaces can be beneficial to interact with these kinds of systems in a variety of settings, as they can be easier to learn and can eliminate several shortcomings of traditional tactile systems, especially for surgical applications. We conducted two user studies that explore different gesture-based interfaces for interaction with volume visualizations. The first experiment focused on rotation tasks, where the performance of the gesture-based interfac

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Liu, Jiaqing, Ryoma Fujii, Tomoko Tateyama, Yutaro Iwamoto, and Yenwei Chen. "Kinect-Based Gesture Recognition for Touchless Visualization of Medical Images." International Journal of Computer and Electrical Engineering 9, no. 2 (2017): 421–29. http://dx.doi.org/10.17706/ijcee.2017.9.2.421-429.

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8

Prabha, Navaneeth, Naeema Ziyad, Navya Prasad, Jisha P. Abraham, Pristy Paul T, and Rini T Paul. "Enhanced Medical Analysis: Leveraging 3D Visualization and VR-AR Technology." Journal of Sensor Networks and Data Communications 4, no. 3 (2024): 01–09. https://doi.org/10.33140/jsndc.04.03.03.

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Modern healthcare depends heavily on medical imaging, but traditional 2D images frequently lack depth and detail. This paper introduces a novel approach, that turns 2D medical images, such as X-rays, MRIs, and CT scans, into immersive three-dimensional visualizations using virtual and augmented reality (VR/AR) technology. The process consists of four steps: acquiring DICOM medical data, converting the data into 3D models, applying the rendering modes and slicing planes, and deploying the data in VR/AR environments. Preprocessing methods evaluate and improve the quality of medical image data, w

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9

Zhou, Liang, Mengjie Fan, Charles Hansen, Chris R. Johnson, and Daniel Weiskopf. "A Review of Three-Dimensional Medical Image Visualization." Health Data Science 2022 (April 5, 2022): 1–19. http://dx.doi.org/10.34133/2022/9840519.

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Importance. Medical images are essential for modern medicine and an important research subject in visualization. However, medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques that could increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Our paper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medical experts and visualization researchers. Highlights. Fundamental visualization techniques are revisited for vari

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10

Hildebrand, C., J. Stausberg, K. H. Englmeier, and G. Kopanitsa. "Visualization of Medical Data Based on EHR Standards." Methods of Information in Medicine 52, no. 01 (2013): 43–50. http://dx.doi.org/10.3414/me12-01-0016.

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SummaryBackground: To organize an efficient interaction between a doctor and an EHR the data has to be presented in the most convenient way. Medical data presentation methods and models must be flexible in order to cover the needs of the users with different backgrounds and requirements. Most visualization methods are doctor oriented, however, there are indications that the involvement of patients can optimize healthcare.Objectives: The research aims at specifying the state of the art of medical data visualization. The paper analyzes a number of projects and defines requirements for a generic

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11

Ganoje, Prayag. "Data Visualization Techniques for Medical Device Performance Analytics." Applied Medical Research 9, no. 2 (2022): 1–4. http://dx.doi.org/10.47363/amr/2022(9)247.

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This research paper explores various data visualization techniques for analyzing and optimizing the performance of medical devices. As medical devices generate vast amounts of data, effective visualization methods are crucial for interpreting this data to enhance device performance, ensure patient safety, and comply with regulatory standards. This paper examines the principles of data visualization, discusses different visualization techniques, and presents case studies of successful implementations. The paper also includes best practices, potential challenges, and future trends in medical dev

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12

PAVLOV, S., S. VYATKIN, and S. ROMANYUK. "MULTILEVEL VOLUMETRIC VISUALIZATION FOR MEDICAL APPLICATIONS." Naukovi praci Donec'kogo nacional'nogo tehnicnogo universitetu. Seria, Informatika, kibernetika i obcisluval'na tehnika 1, no. 26 (2018): 55–62. http://dx.doi.org/10.31474/1996-1588-2018-1-26-55-62.

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13

SAGA, Ryosuke. "AI and Visualization with Medical Data." Journal of the Visualization Society of Japan 38, no. 151 (2018): 19–22. http://dx.doi.org/10.3154/jvs.38.151_19.

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14

Gillmann, Christina, Noeska N. Smit, Eduard Groller, et al. "Ten Open Challenges in Medical Visualization." IEEE Computer Graphics and Applications 41, no. 5 (2021): 7–15. http://dx.doi.org/10.1109/mcg.2021.3094858.

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15

Yanjun Peng, and Yuanhong Wang. "Research on Virtual Medical Visualization System." International Journal of Digital Content Technology and its Applications 7, no. 7 (2013): 804–12. http://dx.doi.org/10.4156/jdcta.vol7.issue7.95.

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16

SUTO, Yasuzo. "Three Dimensional Visualization of Medical Images." Journal of the Visualization Society of Japan 12, no. 45 (1992): 91–100. http://dx.doi.org/10.3154/jvs.12.91.

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17

Odile, Patrick Thalia. "Integrating Art in Medical Visualization Techniques." Research Output Journal of Biological and Applied Science 4, no. 3 (2024): 43–46. http://dx.doi.org/10.59298/rojbas/2024/434346.

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The incorporation of art with medical visualisation techniques offers a significant improvement in the communication of medical information. Traditional methods of medical illustration were primarily concerned with anatomical precision, but introducing artistic aspects can expand knowledge, increase emotional resonance, and produce more effective visual storytelling. This study investigates how artistic principles such as composition, colour theory, and dynamic symmetry can enhance the clarity and effectiveness of medical images. Medical visualisation can go beyond simple representation by com

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18

Suto, Yasuzo. "Three-Dimensional Visualization of Medical Images." Journal of the Society of Mechanical Engineers 96, no. 895 (1993): 488–92. http://dx.doi.org/10.1299/jsmemag.96.895_488.

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19

Zuiderveld, Karel J., Anton H. J. Koning, Rik Stokking, J. B. Antoine Maintz, Fred J. R. Appelman, and Max A. Viergever. "Multimodality visualization of medical volume data." Computers & Graphics 20, no. 6 (1996): 775–91. http://dx.doi.org/10.1016/s0097-8493(96)00050-7.

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20

Abdallah, Yassmin, Abdelaziz Abdelhamid, Taha Elarif, and Abdel-Badeeh M. Salem. "Intelligent Techniques in Medical Volume Visualization." Procedia Computer Science 65 (2015): 546–55. http://dx.doi.org/10.1016/j.procs.2015.09.129.

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21

Deng, Robert H., and Renben Shu. "LAN-based medical visualization communication system." Computer Communications 16, no. 8 (1993): 518–25. http://dx.doi.org/10.1016/0140-3664(93)90067-3.

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22

杨, 帅. "Web-Based Medical Volume Visualization System." Computer Science and Application 08, no. 06 (2018): 937–43. http://dx.doi.org/10.12677/csa.2018.86104.

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23

Dev, P. "Imaging and visualization in medical education." IEEE Computer Graphics and Applications 19, no. 3 (1999): 21–31. http://dx.doi.org/10.1109/38.761545.

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24

Fuchs, H., M. Levoy, and S. M. Pizer. "Interactive visualization of 3D medical data." Computer 22, no. 8 (1989): 46–51. http://dx.doi.org/10.1109/2.35199.

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25

Albarrak, Abdullah M. "Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System." Diagnostics 13, no. 10 (2023): 1733. http://dx.doi.org/10.3390/diagnostics13101733.

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Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. S

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26

Warner, Jeremy L., Joshua C. Denny, David A. Kreda, and Gil Alterovitz. "Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization." Journal of the American Medical Informatics Association 22, no. 2 (2014): 324–29. http://dx.doi.org/10.1136/amiajnl-2014-002965.

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Abstract Our aim was to uncover unrecognized phenomic relationships using force-based network visualization methods, based on observed electronic medical record data. A primary phenotype was defined from actual patient profiles in the Multiparameter Intelligent Monitoring in Intensive Care II database. Network visualizations depicting primary relationships were compared to those incorporating secondary adjacencies. Interactivity was enabled through a phenotype visualization software concept: the Phenomics Advisor. Subendocardial infarction with cardiac arrest was demonstrated as a sample pheno

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27

Elli, Tommaso. "Supporting Literary Criticism with Data Visualization: Four Design Guidelines for Facilitating Interdisciplinary Collaborations." Convergences - Journal of Research and Arts Education 16, no. 32 (2023): 152–63. http://dx.doi.org/10.53681/c1514225187514391s.32.215.

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The application of data visualization in the context of literary criticism opened promising research directions but may raise frictions due to different traditions and approaches into play. Visualizations are commonly designed to promote a “view from nowhere” and appear objective, transparent, and factual. Literary criticism, instead, valorizes individual viewpoints and interpretations. The article presents an inquiry based on Action Research, Participant Observation, and Structured Interviews to explore divergencies between core methodologies of literary criticism (i.e., close reading and int

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28

Lau, Ivan, Ashu Gupta, and Zhonghua Sun. "Clinical Value of Virtual Reality versus 3D Printing in Congenital Heart Disease." Biomolecules 11, no. 6 (2021): 884. http://dx.doi.org/10.3390/biom11060884.

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Both three-dimensional (3D) printing and virtual reality (VR) are reported as being superior to the current visualization techniques in conveying more comprehensive visualization of congenital heart disease (CHD). However, little is known in terms of their clinical value in diagnostic assessment, medical education, and preoperative planning of CHD. This cross-sectional study aims to address these by involving 35 medical practitioners to subjectively evaluate VR visualization of four selected CHD cases in comparison with the corresponding 3D printed heart models (3DPHM). Six questionnaires were

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29

Lohaj, Oliver, Peter Fedačko, and Ján Paralič. "Usability of Medical Data Analysis Tool." Acta Electrotechnica et Informatica 23, no. 4 (2023): 24–29. http://dx.doi.org/10.2478/aei-2023-0019.

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Abstract This article deals with the issue of usability of an exploratory data analysis tool in the field of medicine. The text portion contains a description of the methods and the visualization procedure. It analyses the current state on usability, medical data visualization and presents the benefits of visualization tools. The goal of this research was to design and implement a suitable visualization tool for the provided dataset, which is the result of cooperation between Technical university of Košice and East Slovak Institute of Heart and Vascular Diseases (VÚSCH) and presents cardiovasc

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30

하태준 and Heewon Kye. "Medical data visualization using Unity3D game engine." Journal of the Korea Computer Graphics Society 23, no. 3 (2017): 87–94. http://dx.doi.org/10.15701/kcgs.2017.23.3.87.

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31

Yoshida, Sanchiro. "Visualization of Medical Problems for Early Detection." Open Neuroimaging Journal 12, no. 1 (2018): 66–68. http://dx.doi.org/10.2174/1874440001812010066.

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32

McDonald, J. P., J. P. Siebert, R. J. Fryer, and C. W. Urquhart. "Visualization and model building in medical imaging." Medical Informatics 19, no. 1 (1994): 61–69. http://dx.doi.org/10.3109/14639239409044721.

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33

Harris, Justus. "Sculpting the future of medical data visualization." Cardiovascular Diagnosis and Therapy 8, S1 (2018): S212—S216. http://dx.doi.org/10.21037/cdt.2017.09.10.

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34

Wang, Shou Quan, Wei Feng, and Wei Bo Guo. "A Survey on 3D Medical Image Visualization." Advanced Materials Research 546-547 (July 2012): 416–19. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.416.

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This paper puts forward a survey on the existing 3D medical image visualization methods. These methods are classified into two groups and typical algorithms in each group are described and analyzed. At last future research directions are discussed.

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35

Svakhine, N. A., D. S. Ebert, and W. M. Andrews. "Illustration-Inspired Depth Enhanced Volumetric Medical Visualization." IEEE Transactions on Visualization and Computer Graphics 15, no. 1 (2009): 77–86. http://dx.doi.org/10.1109/tvcg.2008.56.

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36

Zou, Hua, Fu Lin, Jie Han, and Wen Zhang. "GPU-Based Medical Visualization for Large Datasets." Journal of Medical Imaging and Health Informatics 5, no. 7 (2015): 1467–73. http://dx.doi.org/10.1166/jmihi.2015.1560.

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37

Heinonen, Tomi, Kari Visala, Mikko Blomqvist, Hannu Eskola, and Harry Frey. "3D visualization library for multimodal medical images." Computerized Medical Imaging and Graphics 22, no. 4 (1998): 267–73. http://dx.doi.org/10.1016/s0895-6111(98)00031-7.

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38

Lawonn, K., N. N. Smit, K. Bühler, and B. Preim. "A Survey on Multimodal Medical Data Visualization." Computer Graphics Forum 37, no. 1 (2017): 413–38. http://dx.doi.org/10.1111/cgf.13306.

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39

Tatarchuk, Natalya, Jeremy Shopf, and Christopher DeCoro. "Advanced interactive medical visualization on the GPU." Journal of Parallel and Distributed Computing 68, no. 10 (2008): 1319–28. http://dx.doi.org/10.1016/j.jpdc.2008.06.011.

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40

Ristovski, Gordan, Tobias Preusser, Horst K. Hahn, and Lars Linsen. "Uncertainty in medical visualization: Towards a taxonomy." Computers & Graphics 39 (April 2014): 60–73. http://dx.doi.org/10.1016/j.cag.2013.10.015.

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41

Kwon, Koojoo, Dong-Su Kang, Youngihn Kho, and Byeong-Seok Shin. "Medical Contents Visualization System for Smart Device." Journal of Korea Multimedia Society 15, no. 10 (2012): 1264–72. http://dx.doi.org/10.9717/kmms.2012.15.10.1264.

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42

Sayama, Tetsuro, Ataru Nishimura, Ryota Kurogi, et al. "Visualization of Medical Care for Cerebrovascular Disorders." Japanese Journal of Neurosurgery 24, no. 10 (2015): 684–92. http://dx.doi.org/10.7887/jcns.24.684.

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43

Tayupova, Olga I., and Ekaterina V. Poliakova. "Visualization in medical advertising discourse in Germany." Media Linguistics 9, no. 4 (2022): 393–403. http://dx.doi.org/10.21638/spbu22.2022.406.

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A study was made of non-verbal signs used to visualize the printed text of advertising, selected both by belonging to the corresponding subdiscourse and taking into account the territorial feature. Advertising as a type of media text, which has a special composition and influences consumer behavior with the help of specific means, consists of two interrelated codes: a verbal code and a visual code. It has been established that the non-verbal code of this type of text not only transmits information about over-the-counter drugs in order to maintain and enhance sales, but also contains informatio

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44

Kurmangulov, Albert A. "PERCEPTION GENDER FEATURES OF MEDICAL VISUALIZATION SYSTEMS." RSUH/RGGU Bulletin. Series Philosophy. Social Studies. Art Studies, no. 3 (2023): 96–107. http://dx.doi.org/10.28995/2073-6401-2023-3-96-107.

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The article presents the results of an analytical sociological research on the population’s opinion study in the gender aspect regarding the perception and approval of various attributes of the visual image of medical organizations in the Russian Federation. The research found that women are more likely than men to pay attention to design solutions for elements of a visual image. The construction of the Kano matrix made it possible to determine the most attractive attributes of medical imaging systems – a single design of pointers and the presence of graphic elements that received great approv

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45

Ebert, Lars Christian, Sabine Franckenberg, Till Sieberth, et al. "A review of visualization techniques of post-mortem computed tomography data for forensic death investigations." International Journal of Legal Medicine 135, no. 5 (2021): 1855–67. http://dx.doi.org/10.1007/s00414-021-02581-4.

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AbstractPostmortem computed tomography (PMCT) is a standard image modality used in forensic death investigations. Case- and audience-specific visualizations are vital for identifying relevant findings and communicating them appropriately. Different data types and visualization methods exist in 2D and 3D, and all of these types have specific applications. 2D visualizations are more suited for the radiological assessment of PMCT data because they allow the depiction of subtle details. 3D visualizations are better suited for creating visualizations for medical laypersons, such as state attorneys,

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46

Jurgaitis, Jonas, Marius Paškonis, Jonas Pivoriūnas, et al. "The comparison of 2-dimensional with 3-dimensional hepatic visualization in the clinical hepatic anatomy education." Medicina 44, no. 6 (2008): 428. http://dx.doi.org/10.3390/medicina44060056.

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Objective. To determine whether 2-dimensional or 3-dimensional hepatic visualization is better for the medical students to be used while studying the clinical hepatic anatomy. Material and methods. Twenty-nine patients who underwent surgical intervention due to focal hepatic pathology at the Department of General Surgery, University of Heidelberg, and at Clinics of Santariškės, Vilnius University Hospital were included in the retrospective cohort study. Before the surgical intervention, the computed tomography (CT) liver scan and 3- dimensional (3D) hepatic visualization were performed. A tota

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47

Kurmangulov, A. A., N. S. Brynza, and Yu S. Reshetnikova. "Analysis of architectural and planning solutions for visualization systems of medical organizations." Ural Medical Journal 20, no. 4 (2021): 60–66. http://dx.doi.org/10.52420/2071-5943-2021-20-4-60-66.

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Introduction. Currently, visualization is recognized as one of the main criteria for the quality of space of the new model of a medical organization providing primary health care. Purpose of the study to establish the features of architectural and planning solutions for visualization systems of medical organizations providing primary health care. Materials and methods. The object of the study was the visualization systems of 94 medical organizations from seven constituent entities of the Russian Federation, in which all available internal and external visual elements were studied in person. As

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48

Zhang, Yan, Peter J. Passmore, and Richard H. Bayford. "Visualization of multidimensional and multimodal tomographic medical imaging data, a case study." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1900 (2009): 3121–48. http://dx.doi.org/10.1098/rsta.2009.0084.

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Multidimensional tomographic datasets contain physical properties defined over four-dimensional (e.g. spatial–temporal, spatial–spectral), five-dimensional (e.g. spatial–temporal–spectral) or even higher-dimensional domains. Multimodal tomographic datasets contain physical properties obtained with different imaging modalities. In medicine, four-dimensional data are widely used, five-dimensional data are emerging, and multimodal data are being used more often every day. Visualization is vital for medical diagnosis and surgical planning to interpret the information included in imaging data. Visu

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49

Hu, Zhan Li, Jian Bao Gui, Jing Zou, et al. "Real-Time 3D Space Coordinate Acquisition of Medical Visualization Data." Applied Mechanics and Materials 44-47 (December 2010): 3534–37. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3534.

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Medical visualization refers to the techniques and processes used to create images of the human body for clinical purposes or medical science including the study of normal anatomy and physiology. The visualization of medical images data sets is to reconstruct 3D images with the 2D slice images so as to reveal the 3D configuration of organs through human visual system. Visual C++ are used to reconstruct 3D images using the CT slice sequence. The key algorithms and human CT 3D visualization results are given in this paper. The coordinates can be acquired by the mouse clicking in the 3D space, by

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50

Bai, Shengyu, Chen Ma, Xinjun Wang, et al. "Application of Medical Image 3D Visualization Web Platform in Auxiliary Diagnosis and Preoperative Planning." Journal of Image and Graphics 11, no. 1 (2023): 32–39. http://dx.doi.org/10.18178/joig.11.1.32-39.

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Three-dimensional visualization of medical image data can enable doctors to observe images from more angles and higher dimensions. It is of great significance for doctors to assist in diagnosis and preoperative planning. Most 3D visualization systems are based on desktop applications, which are too dependent on hardware and operating system. This makes it difficult to use across platforms and maintain. Web-based systems tend to have limited capabilities. To this end, we developed a web application, which not only provides DICOM (Digital Imaging and Communications in Medicine) image browsing an

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