Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups

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.