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Methods: A two-step approach is presented here. The first step estimates, for each pixel and a given number of directions, a measure for the probability that the point is part of a line segment in the corresponding direction. This can be done efficiently using binary masks. In the second step, a directional filter kernel is applied for pixel that are assumed to be part of a line. For all other pixels a mean filter is used. Results: The proposed algorithm was able to achieve an average contrast to noise ratio (CNR) of 6.3 compared to the bilateral filter with 5.8. For the device segmentation using global thresholding the number of missing or wrong pixels is reduced to 25 % compared to 40 % using the bilateral approach. Conclusion: The proposed algorithm is a simple and efficient approach, which can easily be parallelized for the use on modern graphics processing units. It improves the segmentation results of the device compared to other denoising methods, and therefore reduces artifacts and increases the quality of the reconstruction without increasing the delay in real time applications notably. |
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Image segmentation
Denoising
Reconstruction algorithms
Wavelets
Real time imaging
Binary data
Angiography
