Gaussian filtering

A Gaussian filter is an image-smoothing filter that reduces noise and small intensity variations by replacing each pixel or voxel with a weighted average of its neighbors, where closer neighbors receive higher weights according to a Gaussian (bell-shaped) distribution.

Function¶

To use the Gaussian filter combined with a fixed threshold, use the following function:

seg_gauss(input_image,
    lower_threshold,
    upper_threshold,
    value_in_range,
    value_outside_range,
    sigma,
    support,
    use_image_spacing)

Input settings¶

HR-pQCT

PCD-CT

µCT

input_image: Gray value input image
lower_threshold: Lower threshold for binarization
upper_threshold: Upper threshold for binarization
value_in_range: Value assigned to voxels inside the threshold range. Default = 127
value_outside_range: Value assigned to voxels inside the threshold range. Default = 0
sigma: Gaussian sigma. Default = 0.5
support: Half-width of the Gaussian kernel in voxels. Default = 1.0
use_image_spacing: Whether sigma is interpreted in physical units. Default = False

Further information about the function and its inputs can be found [here].

Examples and workflows¶

HR-pQCT

PCD-CT

µCT

Examples of how to use the function for the Gaussian filtering can be found in:

Workflows that include the Gaussian filter:

Gaussian based segmentation

Citation¶

HR-pQCT

PCD-CT

µCT

If you use this function, please cite it like this:

We used the method as implemented in ORMIR-XCT (Kuczynski et al. (2024)).

References¶

Kuczynski, M. T., Neeteson, N. J., Stok, K. S., Burghardt, A. J., Hernandez, M. A. E., Vicory, J., Tse, J. J., Durongbhan, P., Bonaretti, S., Wong, A. K. O., Boyd, S. K., & Manske, S. L. (2024). ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing. Journal of Open Source Software, 9(97), 6084. 10.21105/joss.06084