# Pyradiomics feature extraction for bin-width 255

**URL:** <https://discourse.slicer.org/t/pyradiomics-feature-extraction-for-bin-width-255/20347>\
**Category:** Radiomics\
**Created:** [October 25, 2021, 11:01pm UTC](https://discourse.slicer.org/t/pyradiomics-feature-extraction-for-bin-width-255/20347 "2021-10-25T23:01:15Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Sovan\_Mukherjee](https://sea2.discourse-cdn.com/flex002/user_avatar/discourse.slicer.org/sovan_mukherjee/32/11886_2.png) [@Sovan\_Mukherjee](https://discourse.slicer.org/u/Sovan_Mukherjee)\
**Post date:** [October 25, 2021, 11:01pm UTC](https://discourse.slicer.org/t/pyradiomics-feature-extraction-for-bin-width-255/20347/1 "2021-10-25T23:01:15Z")

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Hi,

I was extracting radiomics features from a CT dataset using PyRadiomics at various bin-widths for a comparison study (25, 32, 255 etc).

I was comparing individual gray level features between bin-width 25 and 255. My CT images are normalized to intensity levels (0 to 255). So, for bin-width 255, I just have 1 gray level.

While comparing GLCM between bin-width 25 and 255 , I noticed that GLCM for bin-width 255 is almost non-existing which makes sense since I have only one gray level. However, while comparing GLRLM between bin-width 25 and 255, I see a similar pattern between bin-width 25 and 255. I have attached three slides describing what is happening.

My question is for bin-width 255, both GLCM and GLRLM should behave in a similar way since we are dealing with only one gray level. But why, GLRLM feature pattern at bin-width 255 are similar to bin-width 25?

Could you please help me figure it out?

**Version (please complete the following information):**

- OS: [iOS]
- Python version: [3.8.1]
- PyRadiomics version [v3.0.1.post3+g0c53d1d]

Thank you,

 ![Slide1](https://us1.discourse-cdn.com/flex002/uploads/slicer/original/3X/2/f/2fd46d6174c66ff84ecaa86aac862fed90cf89d0.png)  
 ![Slide2](https://us1.discourse-cdn.com/flex002/uploads/slicer/original/3X/c/5/c526774c367941e2efddd872fda5ed16ab539a55.png)  
 ![Slide3](https://us1.discourse-cdn.com/flex002/uploads/slicer/original/3X/7/f/7f6ab87fa6127283d68eb8c8d54fb43613b07ff3.png)