Standardization in the field of medical image management: the contribution of the MIMOSA model.

This paper deals with the development of standards in the field of medical imaging and picture archiving and communication systems (PACS's), and notably concerning the interworking between PACS's and hospital information systems (HIS). It explains, in detail, how a conceptual model of the management of medical images, such as the medical image management in an open system architecture (MIMOSA) model, can contribute to the development of standards for medical image management and PACS's. This contribution is twofold: 1) Since the model lists and structures the concepts and resources involved to make the images available to the users when and where they are required, and describes the interactions between PACS components and HIS, the MIMOSA work helps by defining a reference architecture which includes an external description of the various components of a PACS, and a logical structure for assembling them. 2) The model and the implementation of a demonstrator based on this model allow the relevance of the Digital Imaging and Communications in Medicine (DICOM) standard with respect to image management issues to be assessed, highlighting some current limitations of this standard and proposing extensions. Such a twofold action is necessary in order both to bring solutions, even partial, in the short term, and to allow for the convergence, in the long term, of the standards developed by independent standardization groups in medical informatics (e.g., those within Technical Committee 251 of CEN: Comité Européen de Normalisation).

A medical image object-oriented database with image processing and automatic reorganization capabilities.

The paper presents the medical image database developed for use in the methodological research environment and in the laboratory clinical environment, designed to be capable of being interfaced to an image processing system. This database is intended to solve the numerous problems due to the complexity-multidimensionality and multimodality-of medical images. These problems are posed in terms of management, archiving, structuring and accessing of this archive, and specification of the interfaces with users and with image processing system Solving these problems involves making a formal description of the image and the data associated with the image, while taking into account the specifics of medical imaging. The kernel which contains this description allows the physical architecture of the management and archiving system to be decoupled from its logical architecture. This decoupling is essential in order to automate the recording of new data, the automatic reorganization of the system scheme in the event of change in the system environment, and to help in consulting the database.

A database manager of biomedical images.

In order to allow an efficient archiving and retrieval of biomedical images, PACS would include an image DBMS. As biomedical images have complex structures we developed a specific DBMS over a relational one. The medical image database manager ensures a data description that takes into account data semantics so that consistency or existential constraints may be verified. It provides a unique language to retrieve images no matter what their storage format. It encompasses the queries of the user which constitute a filter over associated data. The manager can handle changes in the environment of the image. We implemented a manager prototype to show the validity of logical access possibilities. The user need no longer know the structure of the image database to access data. The logical access allows retrieval of images, whatever the architecture of the base. An automatic query construction consists of three functional levels including the interfaces towards the external environment which ensure a logical independence from the database, a query generator which creates the correspondence between the user's query and physical access queries, a dictionary which contains the structure of the image data and consistency constraints.

High compression of nuclear medicine dynamic studies.

As data compression plays now an important role in the development of medical PACS, a technique has been developed for medical image sequences storage and transmission in order to obtain very high compression ratio: in dynamic nuclear medicine studies it can achieve a compression ratio as high as 100:1 without significant degradation. The implemented technique combines two methods which multiply their effects. In a first step, a principal component analysis (PCA) of the image series is performed. It extracts a limited number of principal components and their associated images. For data compression it is not necessary to perform an oblique factor analysis to estimate the so-called 'physiological functions' and their spatial distributions as in factor analysis of dynamic structures (FADS). In a second step, the principal images are compressed by means of a transform coding procedure: an adaptive block-quantization technique using the 2D discrete cosine transform (DCT) is implemented, followed by a statistical quantization method to encode the DCT coefficients. To reconstruct the principal images, an inverse DCT is applied. Then the original series is computed from the reconstructed images combined with the principal components which have been stored without any modification. The reconstructed series is compared to the original series, as well as the time activity curves generated on different regions of interest (ROI) and the factor estimates obtained using FADS performed on the two series. Method and evaluation are illustrated on an example of first pass radionuclide angiocardiography.