Bio-medical Ontologies Maintenance and Change Management

Multimedia Medical Databases 77
Generally, these systems have a similar architecture that includes: modules for
medical characteristics extraction from images and their storage in the databases,
modules for content-based retrieval taking into consideration the extracted
characteristics and the user interface [67].
Although the number of the medical informatics systems that implement
efficiently the content-based retrieval process is high, it is used in practice only a
small number of them. An example is IRMA project that brings important
contributions in two research fields [44, 98]:
• Automated classification of radiographs based on global features with respect
to imaging modality, direction, body region examined and biological system
under investigation.
• Identification of image features that are relevant for medical diagnosis. These
features are derived from a-priori classified and registered images.
The system can retrieve images that are similar to a query image taking into
consideration a selected set of features. The research was done on image data
consists of radiographs, but will be extended on medical images from arbitrary
modalities.
Another important CBIR system for the domain of HRCT (High-resolution
Computed Tomography) images of the lung with emphysema-type diseases, is
ASSERT [77, 82]. It was developed at Purdue University in collaboration with the
Department of Radiology at Indiana University and the School of Medicine at the
University of Wisconsin. Because the symptoms of these diseases can drastically
alter the appearance of the texture of the lung, can vary widely across patients and
based on the severity of the disease, ASSERT system characterizes the images
using low-level features like texture features computed from the co-occurrence
matrix of the image. The retrieval is performed hierarchically. At the first level the
disease category of the query image is predicted. At the second level, the most
similar images to the query image that belong to the predicted class are retrieved
and displayed to the user.
Also, it must be mentioned the MedGIFT system, implemented to the
University Hospital from Geneva [69]. It was developed to work together with
CasImage, a radiological teaching file that has been used in daily routine for
several years now. The system works with more than 60,000 images from more
than 10,000 medical cases. The database is available on the Intranet of the
hospital, with a smaller database being publicly available via Internet and MIRC.
The system contains modules for image feature extraction, feature indexing
structures and a communication interface called MRML (Multimedia Retrieval
Mark-up Language).
MedGIFT uses techniques from text retrieval such as [69]:
• Frequency-based feature weights
• Inverted file indexing structures
• Relevance feedback mechanisms