Bio-medical Ontologies Maintenance and Change Management

Multimedia Medical Databases 109
In [108] are specified two main approaches for texture description:
• Descriptive approach- derives a quantitative description of a texture in terms
of a manageable set of feature measures
• Generic approach - creates a geometric or a probability model for texture description
Further, the descriptive approach can be divided into statistical and spectral
methods, because of the techniques used in feature extraction [108].
Statistical methods use as feature descriptors the image signal statistics from
the spatial domain. The most commonly applied statistics are: 1D histograms,
moments, grey-level co-occurrence matrices (GLCM), etc [108]. Usually lowerorder
image statistics, particularly first- and second-order statistics, are used in
texture analysis. First-order statistics (the mean, standard deviation and higherorder
moments of the histogram) work with properties of individual pixels.
Second-order statistics also account for the spatial inter-dependency or cooccurrence
of two pixels at specific relative positions. Grey level co-occurrence
matrices [39], grey level differences [104], autocorrelation function, and local
binary pattern operator [72] are the most commonly applied second-order statistics
for texture description. Higher than second-order statistical features have also
been investigated in [27, 99], but the computational complexity increases
exponentially with the order of statistics. The Haralick features [39] derived from
the GLCM, are one of the most popular feature set.
Spectral methods work in the frequency domain where features are related to
statistics of filter responses [108] and there are several advantages. It was proved
that a tuned bandpass filter bank resembles the structure of the neural receptive
fields in the human visual system [17, 51]. This is the main motivation of spectral
methods to extend feature extraction into the spatial frequency domain.
In constructing filter bank, two-dimensional Gabor filters have been frequently
used [45]. Recently, Leung and Malik identify textons (the cluster centres) as
feature descriptors from filter responses of a stack of training images [58, 59] and
Konishi and Yuille proposed a Bayesian classifier based on the joint probability
distribution of filter responses [49].
Also, the generic approaches can be divided into syntactic and probability
models [108].
Syntactic models analyze the geometric structure of textures with the help of
spatial analytical techniques [108]. The most important methods that belong to this
type of models are fractal analysis and structural approach. A variety of fractal
models have been proposed for modeling textures in natural scenes [75] and in
medical imaging [12].
Probability models generalize the feature based descriptive approach by
deriving a probability model from the joint distribution of selected image features
[108]. Markov-Gibbs random fields are the most successful probability models for
texture analysis [31, 40, 107].