Biomedical Engineering Reference
In-Depth Information
Figure 6.3-3 cont'd
through l show the enhancement of a brain MRI image
with the same steps as above.
neighborhood while maintaining the structure and
function of the operator.
6.3.4.1 Noise suppression by mean
filtering
6.3.4 Local operators
Local operators enhance the image by providing a new
value for each pixel in a manner that depends only on
that pixel and others in a neighborhood around it. Many
local operators are linear spatial filters implemented
with a kernel convolution, some are nonlinear operators,
and others impart histogram equalization within
a neighborhood. In this section we present a set of
estab ishedstandardfilterscommonlyusedforen-
hancement. These can be easily extended to obtain
slightly modified results by increasing the size of the
Mean filtering can be achieved by convolving the image
with a (2
Kþ
1
2
L þ
1) kernel where each coefficient has
a value equal to the reciprocal of the number of coefficients
in the kernel. For example, when
L ¼ K ¼
1, we obtain
8
<
9
=
1
=
9
1
=
9
1
=
9
1
=
9
1
=
9
1
=
9
1
=
9
1
=
9
1
=
9
wðk; lÞ¼
;
:
;
referred to as the 3
3 averaging kernel or mask. Typi-
cally, this type of smoothing reduces noise in the image,
