Bio-medical Ontologies Maintenance and Change Management

108 L. Stanescu, D. Dan Burdescu, and M. Brezovan
Other experimental results can be found in [89, 94].
Several conclusions can be formulated after the experimental results were
analyzed:
• To find images representing the same ill area, that were captured by an endoscope
from several viewing directions, the solution that uses HSV color system
quantized to 166 colors gives the best results
• For images representing the same ill area, captured to different illumination
intensities, the solution that uses l1l2l3l color system quantized to 64 colors,
gives the best results in querying process
• Globally, the solution that uses HSV color space gives most satisfying results,
because the database includes both types of images
In general, for medical images, the first case, with images representing ill area
captured from different angles is the most frequent case. So, that is why the use of
HSV color space, quantized to 166 colors, is recommended. The situation in the
database that was studied was the same, namely, the number of images captured
from different angles was higher than the number of images where only the illumination
intensity was different.
4 Content-Based Visual Query on Texture Feature in
Multimedia Medical Databases
4.1 Overview
There is no precise definition for the notion of texture because the natural textures
present contradicting properties (regularity versus randomness, uniformity versus
distortion) that are very hard to describe in a unified manner. Generally speaking,
the word texture refers to surface characteristics and appearance of an object given
by the size, shape, density, arrangement, proportion of its elementary parts, etc
[108]. A texture is usually described as smooth or rough, soft or hard, coarse of
fine, matt or glossy, etc.
Texture analysis deals with feature extraction and image coding. Feature
extraction tries to identify and select a set of distinguishing and sufficient features
to characterize a texture. Image coding brings out a compact texture description
from selected features. By representing a complex texture with a small number of
parameters automated texture processing is possible [108].
Many texture analysis methods have been proposed in the last years. The
available methods might be categorized into geometrical, statistical, model-based
and signal processing methods [100]. An observation can be made: many
methods apparently stride over more than one above category [108]. For instance,
a Markov-Gibbs Random Field (MGRF) model derives a joint probability
distribution on statistical image features for texture description, so it can be
included in both model-based and statistical categories.