Laplace-Hamming filter

Laplace-Hamming (LH) filtering is an edge-enhancing filtering approach. It combines a Laplace operator, which highlights edges and rapid intensity changes in an image, with a Hamming window, which limits high-frequency noise in an image.

Function¶

To use the LH filter combined with a fixed threshold, use the following commands:

from ormir_xct.core.segmentation.fft_laplace_hamming import fft_laplace_hamming

fft_laplace_hamming(
    image_np, 
    laplace_epsilon, 
    lp_cut_off_freq, 
    hamming_amp)

Input settings¶

HR-pQCT

PCD-CT

µCT

image_np: Gray value input image
laplace_epsilon: Weight of the curvature image. Default = 0.45
lp_cut_off_freq: Low-pass cutoff frequency of the Hamming filter. Default = 0.3
hamming_amp: Amplitude of the Hamming filter. Default = 1.0

Further information about the function and its inputs can be found here (link to the API coming soon!)

Examples and workflows¶

HR-pQCT

PCD-CT

µCT

Examples of how to use the LH filter:

LH filtering example

Workflows that include the LH filter:

Laplace-Hamming based global segmentation

Citation¶

HR-pQCT

PCD-CT

µCT

If you use this function, please cite it as follows:

We used the Laplace-Hamming filter 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