A Simulation Study of Segmentation Methods on the Soil Aggregate Microtomographic Images Wei Wang, Alexandra N. Kravchenko, Kateryna Ananyeva, Alvin J. M. Smucker, C.Y. Lim and Mark L. Rivers Department of Crop & Soil Sciences, Department of Statistics & Probability, MSU Advanced Photon Source, Argonne National Laboratory
Region non-uniformity measure (NU): 0<NU<1 (ground-truth image not required)
Where P and T are the numbers of pore and total number of pixels in the segmented image, and are the variance of grey-scale values in the pore space and total variance in the simulated grayscale image.
Results (Low porosity) Ground truth image IK Entropy Iterative Otsu Distinct segmentation error
Results (Medium porosity) Ground truth image IK Entropy Iterative Otsu
Results (High porosity) Ground truth image IK Entropy Iterative Otsu
Results (High+flow pattern) Ground truth image IK Entropy Iterative Otsu
Comparisons of segmentation methods using ME and NU Overall ranking by ME : IK > Entropy > Iterative > Otsu Overall ranking by NU : IK > Otsu > Iterative > Entropy Indicator Kriging is the best! Indicator Kriging is the best! IK Iter Otsu Entropy Entropy IK Otsu Iter
How good is NU for preserving pore characteristics ?
* Relative error = ( the pore characteristic value from the segmented image - the pore characteristic ground-truth value)/ the ground-truth value