Bio-medical Ontologies Maintenance and Change Management

102 L. Stanescu, D. Dan Burdescu, and M. Brezovan
medical images from arbitrary modalities. As a result, the characteristics that they
focused on were texture and shape [44].
The project I-Browse developed by the City University Honk Kong has as
application domain the Histology of GI Tract. It works with coarse and fine histological
features characterized by color and textures. The color features are represented
by color histograms [42].
Another active framework is ASSERT, developed by University Purdue. It is
specialized to work with gray-level lung images. In this case, each pathology bearing
region (PBR) – region of interest is characterized by a set of shape, texture,
and other gray-level attributes. Here it is also calculated a histogram of the local
gray levels [16, 82].
MedGIFT is the system developed by the University Hospitals of Geneva. It
uses global color histogram based on the HSV (Hue, Saturation, Value) quantized
into 18 hues, 3 saturations, 3 values and 4 grey levels. Also uses local color
blocks. Each image is recursively partitioned into 4 blocks of equal size, and each
block is represented by its mode color. During the experiments, they increased the
number of grey levels used for the color block features and color histogram features
to 8, 16 and 32 [71, 70].
There are presented next some experiments made at University of Craiova,
Faculty of Automation, Computers and Electronics – Software Engineering Department.
The color images from digestive area were acquisitioned using the endoscope.
The reasons that suggested these experimental studies are:
• Most of the content-based visual retrieval systems from medical domain take
into consideration only certain types of images, especially grey-level images. In
this condition, we considered important to consider the color images also, as
they are produced in a high quantity
• There were considered images with diagnostics from digestive area, that was
not so intensely studied
• The conclusions that were presented above corresponds to color spaces experiments
using only nature images and not medical images (that have a higher
complexity)
• The researchers have not established yet the best color space for content-based
visual retrieval on medical multimedia databases.
Experiment 1
The study realized on color images extracted from the DICOM files takes in consideration
three solutions, like:
• The transformation of the RGB color space to HSV and the quantization at 166
colors -M1
• The use of the RGB color space quantized at 64 colors – M2
• The transformation of the RGB color space to the CIE-LUV and the quantization
at 512 colors – M3