Bio-medical Ontologies Maintenance and Change Management

Multimedia Medical Databases 111
of laryngeal disease can be found in [43]. Haralick introduced co-occurrence
matrices for grey-scale textures. They are defined as a histogram, in which the
probability of the simultaneous occurrence of two grey-scale values according to a
predefined neighborhood is stored. Recently, these CMs have been adapted for
color imaging.
In [37, 98] a new method introduced by Tamura et al. was studied for detecting
texture features. They proposed a set of texture features to capture global
texture properties of an image, namely: coarseness, contrast, and directionality.
This information is stored in a three-dimensional histogram, which is quantized to
384 bins.
IRMA is one of the most solid and advanced CBIR systems used in the medical
domain. Texture descriptors are obtained from spatial gray-level difference
statistics, circular Moran autocorrelation function, entropy and coarseness [56].
In [66] the authors describe that in the system medGIFT that derived from
Viper and GNU Image Finding and that was mainly developed for medical
domain at the University Hospitals of Geneva, the local texture features are
detected by partitioning the image and applying Gabor Filters in various scales
and directions. Gabor responses are quantized into 10 strengths. The global texture
features are represented as a simple histogram of the responses of the local Gabor
Filters in various directions and scales.
In [65] the authors consider that for the images gathered in radiology, textures
can be described by wavelet filter responses that measure the changes of the grey
levels in various directions and scales throughout the image, or features derived
from co-occurrence matrices that count the frequency of neighboring grey levels
in various directions and distances. This allows describing the scale of a texture,
the principal directions and whether the changes are very quick or rather gradual.
Texture descriptors make mainly sense when they are extracted from a region that
is homogenous in texture.
A generalized statistical texture analysis technique for characterizing and
recognizing typical, diagnostically most important, vascular patterns relating to
cervical lesions from colposcopic images is made in [46].
A deep presentation of the concepts of texture analysis in general and
specifically in medical imaging can be found in [38]. Magnetic resonance imaging
is the particular focus and the range of established and possible clinical
applications in that modality is dealt with in detail.
A very interesting comparative study on some new methods used for describing
texture feature in medical images is presented in [57]. These methods introduced
by Tamura, Castelli and Ngo are considered as most suitable to distinguish
medical images. Also, in [11] the authors clarify the principles of texture analysis
and give examples of its applications and reviewing studies of the technique.
There are many techniques used for texture extraction, but there isn’t a certain
method that can be considered the most appropriate, this depending on the application
and the type of images taken into account: breast imaging, mammograms, liver, lung