The main objective of this project is to improve the visualization of fractured bones using three image enhancement algorithms: contrast limited adaptive histogram equalization (CLAHE), unsharp masking (UM) and high-frequency emphasis filtering (HEF) After implementing these algorithms, we will discuss about the results and the difficulties found during the development of the project.
Unprocessed images of x-ray films, like this one of a chest, made by Nikola Zivaljevic, M.D. and found at the National Sciente Foundation Multimedia Gallery, will be used in this project.
Besides the first image (001.tif), found at the National Sciente Foundation Multimedia Gallery, all the other images used were founded at the National Library of Medicine MedPix, a free open source database with over 59,000 indexed and curated images, organized, reviewed, approved, and curated free of charge for personal use and for local teaching at institutions.
Unsharp masking is a linear filter that is capable of amplify high-frequencies of an image. The first registry of this technique was made in Germany, during the 30s, in order to get a better resolution for some photos. In modern days, the same filter is used as a feature in present's image editing software, like GIMP or Photoshop.
The first step of the algorithm is to copy the original image and apply a gaussian blur into it (Blur intensity is defined by a setting called Radius).
If we subtract the blurred image from the original image, we will obtain only the edges created by the blur. This is what we call unsharped mask.
Finally, the enhanced image is collected after applying the following formula:
sharpened image = original image + amount * (unsharped mask)
The radius setting is related to the blur intensity (as explained before) because it defines the size of the edges. The amount setting, on the other hand, controls the intensity of the edges (how much dark or light it will be).
Even though this technique is quite simple, it's also very powerful because we can have satisfying results with a linear time complexity O(n).
| Original Image | Enhanced Image |
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It's not really necessary to apply only the gaussian filter in order to get the unsharpened mask. In fact, the project also tested another filters studied at class:
| Original Image | Gaussian Filter | Median Filter | Maximum Filter | Minimum Filter |
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The Contrast Limited Adaptive Histogram Equalization method is a histogram-based method used to improve contrast in images. This technique computes the histogram for the region around each pixel in the image, improving the local contrast and enhancing the edges in each region. Since AHE can overamplify noise in the image, CLAHE prevents this by limiting the amplification.
To apply CLAHE to the images, we first convert them to grayscale and then normalize. This approach is similar to N-CLAHE, but we do not used a log normalization. After this step, the image is padded by reflecting the pixels in the borders, so we can process it all.
Then, to each pixel in the image, we calculate the clipped histogram for the region around it, i.e., we define the maximum number of occurrences a pixel should have. If the occurrence is greater than the clip limit, we cut the exceeding and redistribute to all pixels. To improve this technique, this process can be repeated a certain number of times.
With the clipped histogram, we calculate the probability of each pixel in it and compute the CDF (Cumulative Distribution Function), using the cumulative sum of the ordered pixels, and multiply each value of the function by 255, to limit the image's values to [0, 255]. Since these are float values, the floor operation is used.
After calculating the CDF, all pixels will have a transformation value. We now apply this transformation to the pixel in the center of the region.
This CLAHE implementation expects 3 inputs:
- Window size: size of the rectangular region around the pixel to be processed.
- Clip limit: maximum number of occurrences of the pixel in the histogram.
- Iterations: number of clipping iterations.
As we noticed by processing some images, when the clip limit is very high, the image become noisy. The explanation is that if the limit is very high, there is no clipping, so we fall back to an AHE algorithm.
Since CLAHE computes a histogram to each pixel, its complexity is very high and demands a lot of processing time to finish its task. We had difficulties implementing an optimized version, so the current version is the slow one.
Aside from the high cost, the enhanced image is much better than the unprocessed one, showing hidden features by enhancing its edges and amplifying its visibility. The parameters influenced a lot the results:
- Number of clipping iterations: can reduce noise in the image, but increases drastically the needed computational power and not in all cases it was useful.
- Window size: it was tested a wide range of values for this parameter, since it is the one that influences the most the result. We tested from 8 to 150 and the best one were in [40, 100].
- Clip limit : were tested values from 4 to 150. The best values that were found was 4 and 150, depending on window size and image. The main influence was in the histograms.
To compare the effects of the parameters, several different runs were made with different parameters. The best combination we found was WS = 100, CL = 150 and IT = 1. Depending on the image, IT = 5 yields a great result, as in the Nonossifying Fibroma images. IT > 5 yielded no improvement in the tests. The results can be seen in the next tables.
- WS: window size
- CL: clip limit
- IT: clipping iterations
| Original | WS: 40, CL: 4, IT: 1 | Histogram |
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| Original | WS: 40, CL: 150, IT: 1 | Histogram |
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| Original | WS: 100, CL: 4, IT: 1 | Histogram |
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| Original | WS: 100, CL: 150, IT: 1 | Histogram |
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| Original | WS: 100, CL: 150, IT: 1 | Histogram |
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| Original | WS: 100, CL: 150, IT: 5 | Histogram |
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| Original | WS: 100, CL: 150, IT: 1 | Histogram |
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| Original | WS: 100, CL: 150, IT: 5 | Histogram |
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| Original | Enhanced |
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High-frequency Emphasis filtering is a technique that uses Gaussian High Pass Filter to emphasis and accentuate the edges. The edges tend to be expressed in the high-frequency spectrum since they have more drastic changes of intensity. This technique produce a low contrast image and the use of Histogram Equalization is required to increase both sharpness and contrast.
The first step of the algorithm is to apply a gaussian high pass filter into it (Sharpness intensity is defined by a setting called Radius). The image have to go through the Fourier transformation and the filter function is calculated onto it. After the inverse transformation we will have filtered image. Secondly, the contrast of the image will be adjusted with simple Histogram Equalization:
sharpened image = (original image + (Gaussian Highpass Filter)) * (Histogram Equalization)
High-frequency Emphasis filtering is a kind of 2D Fourier filtering. High pass filter have the value of D0 which is the cut off distance from the center of the shifted, fourier image.
| D0 = 10 | D0 = 90 |
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| Original Image | Enhanced Image |
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For our algorithm's comparisons, we will use a Toddler's Fracture of a Left Tibia from a 3 years old pacient. Children ofter have injuries of the tibia and fibula in their lower limbs.
| Original | UM | HEF | CLAHE |
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Two other images from the same case, a Nonossifying Fibroma from someone who got kicked in the shin while playing soccer, can also be used to check our results.
| Original | UM | HEF | CLAHE |
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| Original | UM | HEF | CLAHE |
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There is a reason why these three algorithms are very known in the literature: all of them returned great result involving x-ray images, allowing us to achieve our project's goal, improving the visibility of fractures in both of the cases above.
It's clear that the CLAHE algorithm, despite being harder to code and optimize, had the best results for our application. The only weak point in this strategy is its running time, much slower than the other two algorithms.
Concluding, if there's available time for the image analysis to be done, the CLAHE algorithm is the recommended one. But if there's not, the UM or HEF can return a satisfactory image without taking so much time.
- André Scheibel de Almada 9292952
- Cho Young Lim 6436060
- Eduardo Garcia Misiuk 9293230
This project was tested in Python 3.10 using venv. Please follow the instructions for your platform on how to install Python.
Clone the repository with git clone https://github.com/asalmada/x-ray-images-enhancement.
Run python3 -m venv x-ray-images-enhancement to install the environment in the repository.
Activate the virtual environment running source x-ray-images-enhancement/bin/activate and then run
pip install -r requirements.txt to install the dependencies.
To run the demonstration, run chmod 755 demo.sh and then ./demo.sh.