Authors:
Teo Manojlović
1
;
Dino Ilić
1
;
Damir Miletić
2
and
Ivan Štajduhar
1
Affiliations:
1
University of Rijeka, Faculty of Engineering, Department of Computer Engineering, Vukovarska 58, 51000 Rijeka, Croatia
;
2
University of Rijeka, Clinical Hospital Centre Rijeka, Clinical Department for Radiology, Krešimirova 42, 51000, Rijeka, Croatia
Keyword(s):
PACS, DICOM, Medical Imaging, Visual Similarity, Clustering, K-medoids.
Abstract:
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.
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