Laplace-Hamming filtering

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 function:

fft_laplace_hamming(
    image_np, 
    laplace_epsilon, 
    lp_cut_off_freq, 
    hamming_amp)

Input settings¶

HR-pQCT

PCD-CT

µCT

image_np: Numpy array of 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].

Examples and workflows¶

HR-pQCT

PCD-CT

µCT

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

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 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