Proceedings Article | 11 May 1994
Yves Bizais, Florent Aubry, Virginie Chameroy, Bernard Gibaud, Franck Lavaire, Herve Garfagni, Andrew Todd-Pokropek, Osman Ratib, Robert Di Paola
KEYWORDS: Image processing, Medical imaging, Data modeling, RGB color model, Digital imaging, Digital image processing, Neodymium, Image compression, Image storage, Angiography
The purpose of this paper is i) to explain the need for a generic image model in medical imaging, ii) to describe under which conditions such a model can be built, and iii) to present the image model we have been developing during the last two years in the framework ofthe EurlPacs IMimosaproject ofthe ATM programme ofthe European Communities'. Several organisations are in the process of defining communication standards (in particular DICOM) for medical imaging, as successfully demonstrated during the last RNSA meeting. Such a standard is an absolute necessity for implementing PACS, since it provides a framework to exchange image information produced by multi-vendor acquisition devices. Unfortunately such a standard is not sufficient to build a clinically useful PACS. One must also describe how data are organised in medical imaging, to allow end users (clinicians) to understand image information. This is the aim of the EurlPacs I Mimosaproject. The basic assumption of this work is that there is a common denominator in the way clinicians "understand" medical images even though local particularisms may hide it. Consequently our model aims at describing medical images in a way general enough to allow for a generic description, while providing facilities to describe local characteristics. Our approach makes use of a fairly standard modelling technique : data model using NIAM, fimctional modelling2 and organisational modelling. It turns out that local particularisms can be described at the dynamic level or even at the implementation level which is not considered in the formal model, such that a generic model can be defined. Moreover communication standards such as DICOM2 can be used within our model to describe how image data are actually organised as files to be transferred between PACS nodes. In this regard there is no overlap between the Mimosa model and communication standards. We consider three levels for the data model : an examination context which describes high level objects such as patient folder, request, report, a PACS model which describes the resources (network, acquisition devices, archives, image workstation) involved in image manipulation, and an image kernel which describes images. The examination context essentially contains attributes allowing HIS/RIS to monitor and control medical image information. They constitute most of the information exchanged between PACS and HIS/RIS. The PACS model addresses issues such as network performances, local storage capacity to provide image information in the right place at the right time. The image kernel specifies image attributes able to accurately define how images are acquired, processed, interpreted and used during diagnostic and/or therapeutic processes. It is clear that this model must be generic and modality independent5 to encompass any and every use of medical images, and precise enough to allow for their efficient use (in particular for multidimensional and multimodality data). Consequently this model may seem complex and significantly differs from commonly used image models. However it proved to be able to describe all examples against which it was tested contrary to other models. Because of its apparent complexity and because of its potential power, we think it is worth devoting a paper to its description. In section 2 we explain why such a model is required. In section 3 we describe the core of the model : the "image object" and its various components : Formal aspects, Version, Representation, Logical Files and Copies. In the same section we present two important related concepts : Image generator and Reference position. In section 4 we show how image objects can be grouped to become meaningful at the examination context level.
