Digital Radiographic Image Enhancement for Improved Visualization

Nisar AhmedSheikh Muhammad Arshad

HITEC University TaxilaDepartment of Electrical Engineering

Supervised By:Dr. Jameel Ahmed

Problem Statement• 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.

Basic Working• 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:-

ProcessingBetter Image

Input Image

Enhancement

Technique

Image Enhancement Techniques

• Contrast Enhancemento Linear Contrast Stretchingo Histogram Equalizationo Contrast Limited Adaptive Histogram Equalizationo Brightness Preserving Histogram Equalization

• Removing Noise o Median filtero Wiener Filtero Sigma Filter

• Image Sharpeningo High Frequency Boosting

o Edge Detection and Enhancement

Image Contrast• 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.

Linear Contrast Stretching

• 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.

Histogram EqualizationHistogram 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.

Contrast Limited Adaptive Histogram Equalization

• 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.

Brightness Preserving Histogram Equalization

• 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.

Results of Contrast Enhancement

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

Comparison of Contrast Enhancement Techniques

Table 1: Contrast EnhancementAdvantages Disadvantages

Linear Contrast Can produce good result by linear stretching.

Can’t produce much attractive results in many cases.

HE This technique is best for visual perception especially when image have close contrast data.

This technique may result in brightness shift because it does not take mean brightness.

CLAHE This technique produces good results when histogram equalization can’t produce attractive results.

This technique produce limited contrast enhancement due to local enhancement.

BPHE Produce best results when HE produces brightness shift.

Take more time duce to separation 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.

Image Noise• 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.

Sigma Filter• 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.

Median filter• 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.

Adaptive Wiener Filter• 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.

Results of Noise Reduction

Results of Noise Reduction

Comparison of Noise Reduction Techniques

Table 2: Noise ReductionAdvantages Disadvantages

Median Filtering Easy to implement. Image edges get blurred. Sigma Filter Easy to implement by adding

threshold in averaging filter. Can’t be used for salt & pepper noise.

Wiener Filter Produce best output when the noise is additive white Gaussian noise.

Require high computational time.

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.

IMAGE SHARPENING• 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.

Conclusion• 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.

Recommendations• 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.

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