1. Nisar Ahmed
Sheikh Muhammad Arshad
HITEC University Taxila Supervised By:
Department of Electrical Engineering Dr. Jameel Ahmed

2. • To develop a complete solution for digital radiographic
image enhancement for improved visualization and better
diagnosis.
• The paper encourages radiologists to become familiar with
these techniques, to evaluate them, and to incorporate
them into specific display protocols.

3. • The aim of image enhancement is to improve the
interpretability or perception of information in image for
human viewers, it case of radiographic images it help the
radiologist in better diagnosis.
• The basic working of the application is:-
Input Image Processing Better Image
Enhancement
Technique

4. • Contrast Enhancement
o Linear Contrast Stretching
o Histogram Equalization
o Contrast Limited Adaptive Histogram Equalization
o Brightness Preserving Histogram Equalization
• Removing Noise
o Median filter
o Wiener Filter
o Sigma Filter
• Image Sharpening
o High Frequency Boosting
o Edge Detection and Enhancement

5. • Image Contrast is the difference in appearance of two or
more parts of an image seen simultaneously. An image
must have good brightness contrast for proper vision. In a
low contrast image we can’t distinguish clearly between
different objects. Increasing the contrast makes the light
areas become lighter and dark areas become darker. We
use different techniques of histogram modification to
improve the visual contrast of the image.
• Histogram is the graph of intensities with number of pixel
lying at those intensity values.

6. • In linear contrast stretching the histogram of image matrix
is linearly stretched over the entire range. This technique
maps the intensities to new values such that the data is
stretched to the whole spectrum. This technique produces
useful results when the histogram of original image is
concentrated in a narrow range of spectrum.
• This technique can’t produce better results in many cases.

7. Histogram equalization generates a gray map which
redistribute all pixel values such as to produce uniform
histogram. Histogram equalization spread out the most
frequent intensity values to allow the areas of lower
contrast to gain a higher contrast.
The principle disadvantages with histogram equalization are:-
o The histogram equalization method may result in over
enhancement and saturation artifacts.
o Histogram equalization can be found on the fact that it may
significantly alter the brightness of an image.

8. • CLAHE computes multiple histograms, each corresponding
to a distinct section to increase local contrast, rather than
overall contrast. The image is divided into tiles and it
operates on tiles rather than the entire image. Contrast of
each tile is enhanced and then all the tiles are combined
using bilinear interpolation to eliminate the artificially
induced boundaries. The contrast especially in
homogeneous areas is limited to avoid noise amplification.
• The principle disadvantage of this technique is it produces
limited contrast enhancement due to local enhancement.

9. • BPHE is used to overcome the problem with simple
histogram equalization. It computes the mean of the image
and decomposes the image into two sub images based on
the mean of the image. One of them is set of samples less
than or equal to the mean whereas the other is the set of
samples greater than the mean. Then two sub images are
equalized independently based on their respective
histograms. Thus the resulting equalized sub images are
bounded by each other around the mean, which has an
effect of preserving mean brightness.
• The only problem with this technique is, is take more
computational time them histogram equalization.

10. The above images shows, in majority of cases adaptive histogram equalization
produces best result. Brightness preserving histogram equalization can be used if
needed.

11. Table 1: Contrast Enhancement
Advantages Disadvantages
Linear Contrast Can produce good result by linear Can’t produce much attractive results
stretching. in many cases.
HE This technique is best for visual This technique may result in
perception especially when image brightness shift because it does not
have close contrast data. take mean brightness.
CLAHE This technique produces good results This technique produce limited
when histogram equalization can’t contrast enhancement due to local
produce attractive results. enhancement.
BPHE Produce best results when HE Take more time duce to separation
produces brightness shift. into two images and appending after
their enhancement.
The best among the above discussed technique is BPHE (brightness
preserving histogram equalization) it produces good result while
preserving the image mean brightness.

12. • Noise is the result of errors in the image acquisition
process that result in pixel values that do not reflect the
true intensities of the real organ.
• Radiographic images are prone to a variety of types of
noise due to several reasons such as:-
o If the image is scanned from an X-Ray film or CT image, the film
grain is a source of noise. It can be a result of a damaged film or due
to the scanner itself.
o If the image is captured directly from digital X-Ray scanner or a CT
scanner it can be due to mechanism of gathering the data.

13. • Linear Filtering is easiest method to remove certain type of
noise. Averaging or mean filter can be used to accomplish this
job. In averaging filter each pixel gets set to the average of its
neighboring pixels. The problem with averaging filter is that
edges of image get blurred.
• To overcome this problem we use selective mean filter such as
sigma filter. It preserves edges better and is less sensitive to
edges.
• The filter smoothes an image by taking an average over the
neighboring pixels, but only includes those pixels that have a
value not deviating from the current pixel by more than a given
range.
• Edges having a value very different from the surrounding are not
included in the average and, thus, completely eliminated from
blurring.

14. • Median filter works in a similar way as averaging filter, the
only difference is the output value of a pixel is determined
by the median of the neighboring pixel rather than mean.
• The principle advantage of median filtering over averaging
is that it is much less sensitive to extreme values. Therefore
median filtering is better to remove noise while reducing
the blurring of edges.

15. • Wiener filter often produce much better results than linear
filter. It uses a pixel wise adaptive Wiener method based on
statistics estimated from a local neighborhood of each
pixel..
• This filter produces best output when noise is AWGN.
• The problem of this method over the previous one is it
requires more computational time.

18. Table 2: Noise Reduction
Advantages Disadvantages
Median Filtering Easy to implement. Image edges get blurred.
Sigma Filter Easy to implement by adding Can’t be used for salt & pepper noise.
threshold in averaging filter.
Wiener Filter Produce best output when the noise is Require high computational time.
additive white Gaussian noise.
Sigma filter produce better result for CT and MRI images. It preserves the
edges while removing the noise. Threshold can be adjusted to acquire the
desired performance. However median filter also reduce noise effectively.
Its results become good if we apply image sharpening filter after median
filtering.

19. • In radiology, we want the recorded image to be a faithful
representation of the organs that we want to see but every
image is more or less blurry because image information
spills over to neighboring pixels.
• When the image does not shows sharp details of its
features it is called blurred image. Thus, image sharpening
is fundamental in making images clear and useful.
• Image sharpening using Laplacian of Gaussian filter has
been used for CT or MRI images and high frequency
component boosting has been used in X-Ray and
mammographic images.

20. • Contrast enhancement using histogram processing is an
effective method, four techniques of histogram processing
has been applied on a large number of digital radiographic
images. BPHE has shown best results in majority of cases.
Three techniques have been used for noise reduction
among them sigma filter has shown better results.
However median filter followed by image sharpening also
show good results.

21. • Various aspects of image enhancement are catered for in
the implementation and subsequent exercise of results,
nevertheless, we understand that it is so demanding and
absorbing area for research that the work could
substantially be carried forward in following directions as
a future work:
o Improvement in selective noise reduction techniques.
o Level correction of image by combining it with image segmentation.