S S S A2009 Simulation Study Of SegmentationPresentation Transcript
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
where P and S are the number of common pore or solid pixels in both ground-truth and segmented images.
Segmentation performance criterion
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.
Whether NU can be used as a criterion for soil ?
How good is NU for soil ?
Pore morphological characteristics:
Number of connected pores
Number of pore boundary pixels
Number of pore skeleton pixels
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
Soil aggregate CMT images were generated from the pore/solid binary image by simulating partial volume effect, different solid material and background noise.
No single method preserved pore characteristics in all cases. However, Indicator Kriging method yielded segmented images most similar to the ground-truth images in the majority of cases studied.
We recommend using NU as a criterion for choosing best segmentation approaches.
Segmentation assessment using NU provides acceptable representation of pore characteristics in the segmented images.
USDA-CSREES National Research Initiative:
NSF LTER Program at Kellogg Biological Station and the Michigan Agricultural Experiment Station
Advanced Photon Source, Argonne National Lab
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