Biomedical Engineering Reference
In-Depth Information
Figure 6.3-2 (a) Input image where details of interest are in the 90-to-170 gray level band. This intensity band identifies the bony
structures in this image and provides an example of a feature that may be of dental interest. (b) Histogram of the input image in (a).
(c) This output image selectively shows the intensity band of interest stretched over the entire dynamic range of the display. This specific
enhancement may be potentially useful in highlighting features or characteristics of bony tissue in dental X-ray imagery. This technique
may also be effective in focusing attention on other image features such as bony lamina dura or recurrent caries. (d) Histogram of the
output image in (c). This histogram shows the gray levels in the original image in the 90-to-170 intensity band stretched over 0 to 255.
be more useful to maximize the information conveyed
from the image to the user by distributing the intensity
information in the image as uniformly as possible over the
available intensity band [3, 6, 7] . This approach is based
on an approximate realization of an information-theo-
retic approach in which the normalized histogram of the
image is interpreted as the probability density function of
the intensity of the image. In histogram equalization, the
histogram of the input image is mapped to a new maxi-
mally-flat histogram.
As indicated in Section 6.3.2, the histogram is de-
fined as h ( i ), with 0 to P-1 gray levels in the image.
The total number of pixels in the image, M * N, is also
the sum of all the values in h ( i ). Thus, in order to
distribute most uniformly the intensity profile of the
image, each bin of the histogram should have a pixel
count of ( M * N )/ P.
It is, in general, possible to move the pixels with
a given intensity to another intensity, resulting in an in-
crease in the pixel count in the new intensity bin. On the
other hand, there is no acceptable way to reduce or
divide the pixel count at a specific intensity in order to
reduce the pixel count to the desired ( M * N )/ P. In order
to achieve approximate uniformity, the average value of
the pixel count over a number of pixel values can be
made close to the uniform level.
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