Bio-medical Ontologies Maintenance and Change Management

130 L. Stanescu, D. Dan Burdescu, and M. Brezovan
Region 1 Region 2 Region 3
Region 4 Region 5 Region 6
Fig. 5.2. Color regions detected by the algorithm
For each region, the color, the minimum bounding rectangle and the number of
pixels that roughly indicates the dimension of the sick region, were stored in the database.
The region localization is given by the minimal bounding rectangle (MBR). The
region area is represented by the number of color pixels, and can be smaller than the
minimum-bounding rectangle. Other experiments can be found in [90].
5.3 Content-Based Region Query – Experiments and Results
The regions detected by the image automatic segmentation techniques can be utilized
in content-based region query of the multimedia medical databases [86].
In a content-based region query, the images are compared based on their regions.
In the first step of the query, content-based visual queries are effectuated on
the regions, and not on the images. Then, in the final step of the query, there are
determined the images corresponding to the regions and there is computed the
total distance between the images by the weighting of the distances between regions.
So, the user selects one or several query regions in order to find in the database
images containing similar regions from the color, size or spatial extent point
of view.
The content–based visual query may be improved by adding spatial information
to the query. So, the total measure of the dissimilarity takes into consideration
both the values of the features (color, texture), and the spatial values of the regions.
There are two types of spatial indexing, namely: relative and absolute [83,
86, 87, 91].
The most powerful images retrieval system is the one that allows queries in
which are specified both the visual features and spatial properties for the desired
images. Such query offers to the user the possibility to control the selection of