Biomedical Engineering Reference
In-Depth Information
Figure 6.3-3 cont'd
but at the expense of the sharpness of edges
[4, 5, 12, 13]
.
Examples of the application of this kernel are seen in
Fig. 6.3-4a-6.3-4d
. Note that the size of the kernel is
a critical factor in the successful application of this type of
enhancement. Image details that are small relative to the
size of the kernel are significantly suppressed, while image
details significantly larger than the kernel size are affected
moderately. The degree of noise suppression is related to
the size of the kernel, with greater suppression achieved
by larger kernels.
convolution to process the image with a kernel of co-
efficients. Rather, in each position of the kernel frame,
a pixel of the input image contained in the frame is se-
lected to become the output pixel located at the co-
ordinates of the kernel center. The kernel frame is
centered on each pixel (
m, n
) of the original image, and the
median value of pixels within the kernel frame is com-
puted. The pixel at the coordinates (
m, n
) of the output
image is set to this median value. In general, median filters
do not have the same smoothing characteristics as the
mean filter
[4, 5, 8, 9, 15]
. Features that are smaller than
half the size of the median filter kernel are completely
removed by the filter. Large discontinuities such as edges
and large changes in image intensity are not affected in
terms of gray level intensity by the median filter, although
their positions may be shifted by a few pixels. This
6.3.4.2 Noise suppression by median
filtering
Median filtering is a common nonlinear method for noise
suppression that has unique characteristics. It does not use
