Bio-medical Ontologies Maintenance and Change Management

110 L. Stanescu, D. Dan Burdescu, and M. Brezovan
Together with color, texture is a powerful characteristic of medical images too,
where a disease can be indicated by changes in the color and texture of a tissue.
Many methods have been studied to extract texture feature from medical images.
The number of publications that presents these methods from the medical domain
is impressive. Reading only a small part of them and some articles that presents an
overview for them, it can be said that the most used techniques for texture
detection in the medical images, are:
• Wavelets
• Gabor Filters
• Co-occurrence matrices
• Based on Fourier transform
• Markov-Gibbs Random Field
• Tamura method
In [95] the authors present a 2-stage method based on wavelet transforms for
detecting and segmenting calcifications in mammograms that may be an early sign
of disease. Individual grains are difficult to detect and segment due to size and
shape variability and because the background mammogram texture is typically
inhomogeneous.
The same wavelet transform was used and presented in [25] to carry out the
supervised segmentation of echographic images corresponding to injured Achilles
tendon of athletes. Texture features are calculated on the expansion wavelet
coefficients of the images. The Mahalanobis distance between texture samples of
the injured tissue and pattern texture is computed and used as a discriminating
function.
Cardiac image properties are analyzed and evaluated with the help of Gabor
filters [34]. The paper shows that in the case of cardiac imaging, these techniques
can be used for indexing, retrieval by similarity queries, and to some extent,
extracting clinically relevant information from the images.
In [63] there is presented a method for texture extraction from medical images
that is different from the others presented above. It is based on vector
quantization, a technique used in images compression.
An experimental study on a database with different human body tissues is
presented in [24]. The study takes into consideration the following texture
description: Gradient, entropy, homogeneity, variance, 3 th moment, inverse
variance and energy, based on the co-occurrence matrices method. The Gradient
method is a new method proposed by authors.
In [4] can be found a study that is a preliminary preparation for the application
of some methods to medical images. It is presented a statistical approach of the
texture description. Specifically, it introduces the use of first- and second-order
statistics on texture color spaces.
An evaluation of texture analysis based on co-occurrence matrices (CMs) with
respect to clinical data derived from laryngeal images with and without organic
disease in order to examine the feasibility of objective computer-based evaluation