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Scientific paper - Conference Proceedings Article (invited lecture)
Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups
In: Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods. Portugal: Science and Technology Publications, 2020. pp. 510-517. https://doi.org/10.5220/0008973405100517
Manojlović, Teo; Ilić, Dino; Miletić, Damir; Štajduhar, Ivan

Institutional repository: Repository of the University of Rijeka, Faculty of Medicine

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Manojlović, T., Ilić, D., Miletić, D. & Štajduhar, I. (2020). Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups. Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (pp. 510-517). Portugal: Science and Technology Publications. doi: 10.5220/0008973405100517

Manojlović, Teo, et al. "Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups." Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, Science and Technology Publications, 2020, pp. 510-517. https://urn.nsk.hr/urn:nbn:hr:184:351926

Manojlović, Teo, Dino Ilić, Damir Miletić and Ivan Štajduhar. "Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups." In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, 510-517. Portugal: Science and Technology Publications, 2020. Accessed 2024 November 26. https://urn.nsk.hr/urn:nbn:hr:184:351926

Manojlović, T., et al. (2020) 'Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups', Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, Science and Technology Publications, Portugal, pp. 510-517. Available at: https://urn.nsk.hr/urn:nbn:hr:184:351926 (Accessed 26 November 2024)

Manojlović T, Ilić D, Miletić D, Štajduhar I. Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups. Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods. Portugal: Science and Technology Publications; 2020. Pp. 510-517. [cited 2024 November 26] Available at: https://urn.nsk.hr/urn:nbn:hr:184:351926

T. Manojlović, D. Ilić, D. Miletić and I. Štajduhar, "Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups", Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods Portugal: Science and Technology Publications, 2020. [Online] Available at: https://urn.nsk.hr/urn:nbn:hr:184:351926 [Accessed: 26 November 2024]

Metadata
Title (english)Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups
AuthorTeo Manojlović
AuthorDino Ilić
AuthorDamir Miletić
AuthorIvan Štajduhar
Author's institutionUniversity of Rijeka
Faculty of Medicine
(Department of Radiology)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Radiology
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract (english)
The data stored in a Picture Archiving and Communication System (PACS) of a clinical centre normally consists of medical images recorded from patients using select imaging techniques, and stored metadata information concerning the details on the conducted diagnostic procedures - the latter being commonly stored using the Digital Imaging and Communications in Medicine (DICOM) standard. In this work, we explore the possibility of utilising DICOM tags for automatic annotation of PACS databases, using K-medoids clustering. We gather and analyse DICOM data of medical radiology images available as a part of the RadiologyNet database, which was built in 2017, and originates from the Clinical Hospital Centre Rijeka, Croatia. Following data preprocessing, we used K-medoids clustering for multiple values of K, and we chose the most appropriate number of clusters based on the silhouette score. Next, for evaluating the clustering performance with regard to the visual similarity of images, we trained an autoencoder from a non-overlapping set of images. That way, we estimated the visual similarity of pixel data clustered by DICOM tags. Paired t-test (p < 0.001) suggests a significant difference between the mean distance from cluster centres of images clustered by DICOM tags, and randomly-permuted cluster labels.
Keywords (english)
Languageenglish
Publication typeScientific paper - Conference Proceedings Article (invited lecture)
Publication statusPublished
Peer reviewUnknown
Publication versionPublished version
Publication titleProceedings of the 9th International Conference on Pattern Recognition Applications and Methods
Numberingno. 1, pp. 510-517
ISBN978-989-758-397-1
p-ISSN2184-4313
DOIhttps://doi.org/10.5220/0008973405100517
URN:NBNurn:nbn:hr:184:351926
Publication2020
ConferenceTitle: 9th International Conference on Pattern Recognition Applications and Methods
Acronym: ICPAM 2020
Location: Valletta, Malta
Start date: 2020-02-22
End date: 2020-02-24
Assembly type: Lecture
Type of resourceText
PublisherScience and Technology Publications
Publishing placePortugal
Access conditionsOpen access
Terms of useLicence Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) icon
Created on2020-03-29 13:45:07