The IRMA code for unique classification of medical images

Modern communication standards such as Digital Imaging and Communication in Medicine (DICOM) include non-image data for a standardized description of study, patient, or technical parameters. However, these tags are rather roughly structured, ambiguous, and often optional. In this paper, we present a mono-hierarchical multi-axial classification code for medical images and emphasize its advantages for content-based image retrieval in medical applications (IRMA). Our so called IRMA coding system consists of four axes with three to four positions, each in {0,...9,a,...,z}, where "0" denotes "unspecified" to determine the end of a path along an axis. In particular, the technical code (T) describes the imaging modality; the directional code (D) models body orientations; the anatomical code (A) refers to the body region examined; and the biological code (B) describes the biological system examined. Hence, the entire code results in a character string of not more than 13 characters (IRMA: TTTT - DDD - AAA - BBB). The code can be easily extended by introducing characters in certain code positions, e.g., if new modalities are introduced. In contrast to other approaches, mixtures of one- and two-literal code positions are avoided which simplifies automatic code processing. Furthermore, the IRMA code obviates ambiguities resulting from overlapping code elements within the same level. Although this code was originally designed to be used in the IRMA project, other use of it is welcome.

[1]  Michael Kohnen,et al.  Detailed image classification code for image retrieval of medical images (IRMA) , 2002 .

[2]  James R. Campbell,et al.  n Phase II Evaluation of Clinical Coding Schemes : Completeness , Taxonomy , Mapping , Definitions , and Clarity , 2022 .

[3]  Kent A. Spackman,et al.  Controlled terminology for clinically-relevant indexing and selective retrieval of biomedical images , 1997, International Journal on Digital Libraries.

[4]  Michael Kohnen,et al.  Quality of DICOM header information for image categorization , 2002, SPIE Medical Imaging.

[5]  Thomas Martin Deserno,et al.  A monohierarchical multiaxial classification code for medical images in content-based retrieval , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[6]  Yoshiharu Sukenobu,et al.  JJ1017 image examination order codes: standardized codes supplementary to DICOM for imaging modality, region, and direction , 2002, SPIE Medical Imaging.

[7]  W D Bidgood,et al.  The SNOMED DICOM microglossary: controlled terminology resource for data interchange in biomedical imaging. , 1998, Methods of information in medicine.

[8]  James S. Duncan,et al.  Synthesis of Research: Medical Image Databases: A Content-based Retrieval Approach , 1997, J. Am. Medical Informatics Assoc..

[9]  J. M. Ollinger,et al.  Positron Emission Tomography , 2018, Handbook of Small Animal Imaging.

[10]  Thomas M. Lehmanna,et al.  Header for SPIE use Content-Based Image Retrieval in Medical Applications: A Novel Multi-Step Approach , 2022 .

[11]  R. Côté.  Systematized Nomenclature of Medicine , 1979 .

[12]  Thomas Martin Deserno,et al.  Content-based image retrieval in medical applications: a novel multistep approach , 1999, Electronic Imaging.