Bio-medical Ontologies Maintenance and Change Management

78 L. Stanescu, D. Dan Burdescu, and M. Brezovan
Four feature groups represent the image content [69]:
• Local and global texture features based on responses of Gabor filters;
• Color/grey scale characteristics on a global image scale and locally within image
regions.
The interface allows for an easy integration into applications such as [69]:
• Teaching file
• Document management systems
• Tools for diagnostic aid.
At the Software Engineering Department, Faculty of Automation, Computers
and Electronics (A.C.E.) Craiova an academic software tool with multi-threaded
client/server architecture was developed. MIR (Medical Image Retrieval) includes
the following functions [92]:
• The processing of the DICOM standard files provided by the medical tools in
order to extract the alphanumeric data and images
• Storing the resulting data in a database
• Extracting the color feature from the image (the color histogram and the binary
color set resulting from the HSV color space quantized to 166 colors)
• Extracting the color texture feature from the color images (using the cooccurrence
matrices and the Gabor filters)
• Detecting color regions and storing the new data in the database
• The simple text-based query of the database
• The content-based visual query of the database on color and color texture
features
• The content-based region query of the database
The medical personnel taking into consideration aspects like execution speed,
retrieval quality and new necessary options test the software tool now. The MySql
database contains 960 color images from the digestive area gathered with an
endoscope.
Also, many experimental studies in the field of content-based visual retrieval
on medical databases with color endoscopic images were effectuated in
collaboration with University Hospital and Filantropia University Hospital from
Craiova.
There is a large variety of applications and studies of content-based visual
retrieval that takes into consideration the images from different medical
departments. Most of them use databases with images produced in radiology
departments, but there are many other departments where this type of algorithms
have been implemented. Some of these categories of images are [67]:
• Dermatologic images
• Cytological specimens