Bio-medical Ontologies Maintenance and Change Management

112 L. Stanescu, D. Dan Burdescu, and M. Brezovan
or cardiac images. There are made efforts for finding a texture detection method for
medical images that produce good results, regardless of the type of images [67].
Although most images coming from nature and other fields are color, the
majority of research has been done on grayscale textures, for several reasons: high
costs for color cameras, high computational costs for color image processing,
large complexity even for grayscale textures. However, over the past few years,
research has been done in color textures recognition, proving that taking into
account the color information improves the color texture classification [74].
In [67], the authors concluded that there are only few comparative studies for
the methods used in texture detections and cannot be determined which of them
produces the best results from the quality and complexity point of view.
That is why, we proposed ourselves to make a comparative study of two
techniques for texture detection that are mostly used: Gabor filters and cooccurrence
matrices. An element of originality of this study is that it takes into
consideration color texture detection on images from databases with medical
images from the digestive acquired using an endoscope, from patients with
different diagnosis.
4.2 Gabor Filters
Starting from the representation of the HSV color space, the color in complex
domain can be represented. The affix of any point from the HSV cone base can be
computed as [74, 106]: z M = S (cos H + i sin H). Therefore, the saturation is
interpreted as the magnitude and the hue as the phase of the complex value b; the
value channel is not included. The advantages of this representation of complex
color are: the simplicity due to the fact that the color is now a scalar and not a
vector and the combination between channels is done before filtering. In
conclusion, the color can be represented in complex domain [74]:
iH(x, y)
b(x, y) = S(x, y) ⋅e
(4.1)
The computation of the Gabor characteristics for the image represented in the HScomplex
space is similar to the one for the monochromatic Gabor characteristics,
because the combination of color channels is done before filtering [74]:
C = (
f, ϕ ∑
y
x ,
( FFT
−1
{P(u, v) ⋅ M (u, v)}))
f, ϕ
The Gabor characteristics vector is created using the value computed
for 3 scales and 4 orientations [74]:
2
(4.2)
f = ( C 0 , 0 , C 0 , 1,...,
C 2 , 3 )
(4.3)
The similarity between the texture characteristics of the query image Q and the
target image T is defined by the metric [74]:
C
f, ϕ