Module: MeasureGranularity

Measure Granularity outputs spectra of size measurements of the textures in the image.
Image granularity is a texture measurement that tries a series of structure elements of increasing size and outputs a spectrum of measures of how well these structure elements fit in the texture of the image. Granularity is measured as described by Ilya Ravkin (references below). The size of the starting structure element as well as the range of the spectrum is given as input.

Available measurements



Select an image to measure

Select the grayscale images whose granularity you want to measure.

Subsampling factor for granularity measurements

If the textures of interest are larger than a few pixels, we recommend you subsample the image with a factor <1 to speed up the processing. Down sampling the image will let you detect larger structures with a smaller sized structure element. A factor >1 might increase the accuracy but also require more processing time. Images are typically of higher resolution than is required for granularity measurements, so the default value is 0.25. For low-resolution images, increase the subsampling fraction; for high-resolution images, decrease the subsampling fraction. Subsampling by 1/4 reduces computation time by (1/4)3 because the size of the image is (1/4)2 of original and the range of granular spectrum can be 1/4 of original. Moreover, the results are sometimes actually a little better with subsampling, which is probably because with subsampling the individual granular spectrum components can be used as features, whereas without subsampling a feature should be a sum of several adjacent granular spectrum components. The recommendation on the numerical value cannot be determined in advance; an analysis as in this reference may be required before running the whole set. See this pdf, slides 27-31, 49-50.

Subsampling factor for background reduction

It is important to remove low frequency image background variations as they will affect the final granularity measurement. Any method can be used as a pre-processing step prior to this module; we have chosen to simply subtract a highly open image. To do it quickly, we subsample the image first. The subsampling factor for background reduction is usually [0.125 – 0.25]. This is highly empirical, but a small factor should be used if the structures of interest are large. The significance of background removal in the context of granulometry is that image volume at certain granular size is normalized by total image volume, which depends on how the background was removed.

Radius of structuring element

This radius should correspond to the radius of the textures of interest after subsampling; i.e., if textures in the original image scale have a radius of 40 pixels, and a subsampling factor of 0.25 is used, the structuring element size should be 10 or slightly smaller, and the range of the spectrum defined below will cover more sizes.

Range of the granular spectrum

You may need a trial run to see which granular spectrum range yields informative measurements. Start by using a wide spectrum and narrow it down to the informative range to save time.