Clipping for windowing

[emi-dm]

Hi guys! Why is ScaleIntensityRange clip performed after MinMax normalization? Is that correct? Does it follow the original implementation of windowing technique?

Thanks!

[KumoLiu]

Hi @emi-dm, thanks for your interest here.
The clip here is used to ensure that the output values of the normalization process stay within the specified output range [b_min, b_max].

• Floating-point arithmetic can introduce small inaccuracies. Even if the calculations are mathematically correct, the limitations of floating-point representation can cause values to slightly exceed the expected range.
• If the input image contains values outside the specified range [a_min, a_max], these values will be scaled outside the desired output range after normalization. This can happen if there are unexpected intensities in the image due to noise or artifacts.
  So the clip argument acts as a safeguard to ensure that the normalized data adheres strictly to the desired output range [b_min, b_max]

Hope it helps, thanks.

[emi-dm]

Thank you so much guy @KumoLiu! It's a pleasure for me posting my personal questions here and getting this warm support!

[emi-dm]

I leave here a little example related to the question:

import numpy as np

# Example input data (image intensities), in the range [a_min, a_max]
image = np.array([10, 50, 120, 200, 255])

# Specified input range [a_min, a_max]
a_min, a_max = 0, 255

# Desired output range [b_min, b_max]
b_min, b_max = 0, 1

# Normalization formula: (value - a_min) / (a_max - a_min) * (b_max - b_min) + b_min

# Case 1: Normalization without clipping beforehand
normalized = (image - a_min) / (a_max - a_min) * (b_max - b_min) + b_min
# Here, some values might fall slightly outside [b_min, b_max] due to floating-point inaccuracies
normalized_clipped = np.clip(normalized, b_min, b_max)

print("Normalized without clipping first:", normalized)
print("Normalized values clipped to [b_min, b_max]:", normalized_clipped)

# Case 2: Applying clipping before normalization (if needed)
clipped_image = np.clip(image, a_min, a_max)
normalized_after_clip = (clipped_image - a_min) / (a_max - a_min) * (b_max - b_min) + b_min

print("Image clipped before normalization:", clipped_image)
print("Normalized values after clipping image:", normalized_after_clip)