Modern communication standards such as Digital Imaging and Communication in Medicine (DICOM) include nonimage 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.

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The widely used DICOM 3.0 imaging protocol specifies optional tags to store specific information on modality and body region within the header: Body Part Examined and Anatomic Structure. We investigate whether this information can be used for the automated categorization of medical images, as this is an important first step for medical image retrieval. Our survey examines the headers generated by four digital image modalities (2 CTs, 2 MRIs) in clinical routine at the Aachen University Hospital within a period of four months. The manufacturing dates of the modalities range from 1995 to 1999, with software revisions from 1999 and 2000. Only one modality sets the DICOM tag Body Part Examined. 90 out of 580 images (15.5%) contained false tag entries causing a wrong categorization. This result was verified during a second evaluation period of one month one year later (562 images, 15.3% error rate). The main reason is the dependency of the tag on the examination protocol of the mOdality, which controls all relevant parameters of the imaging process. In routine, the clinical personnel often applies an examination protocol outside its normal context to improve the imaging quality. This is, however, done without manually adjusting the categorization specific tag values. The values specified by DICOM for the tag Body Part Examined are insufficient to encode the anatomic region precisely. Thus, an automated categorization relying on DICOM tags alone is impossible.

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Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.