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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
Solid space simulation was done for all the “ white ” pixels using spatial simulation of LU decomposition technique
Gaussian random noise was added to the whole image
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Grey scale image simulation Ground truth image Simulation in the pore space Simulation in the solid space + noise simulation Original image from the scan
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Different porosity cases (1) Low Medium High High + flow pattern Porosity = 4.8% Porosity = 7.8% Porosity = 16.5% Porosity = 22.8%
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Different porosity cases (2) Low Medium High High + flow pattern Porosity = 3.6% Porosity = 8.3% Porosity = 15.8% Porosity = 28.5%
Thresholding step: the thresholds are determined by fitting mixed Gaussian distributions to pore and solid spaces using Expectation-Maximization algorithm (Dempster et al., 1977 ).
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.
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Results (Low porosity) Ground truth image IK Entropy Iterative Otsu Distinct segmentation error
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Results (Medium porosity) Ground truth image IK Entropy Iterative Otsu
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Results (High porosity) Ground truth image IK Entropy Iterative Otsu
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Results (High+flow pattern) Ground truth image IK Entropy Iterative Otsu
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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
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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.
M. Sezgin, B. Sankur. 2004. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146 – 165
P. Sahoo, C. Wilkins and J. Yeager. 1997. Threshold selection using Renyi ’ s entropy. Pattern Recognition, Vol.1, No.1, 71-84
W. Oh, B. Lindquist. 1999. Image thesholding by indicator kriging. IEEE Transactions on Pattern Analysis and Machine Intelligence 21: 590-602.
T. W. Ridler and S. Calvard, ‘‘ Picture thresholding using an iterative selection method, ’’ IEEE Trans. Syst. Man Cybern. SMC-8, 630 – 632 ~1978.
N. Otsu (1979). "A threshold selection method from gray-level histograms". IEEE Trans. Sys., Man., Cyber. 9: 62 – 66
Dempster, A.P., Laird, N.M. and Rubin, D.B., 1977. Maximum likelihood from in- complete data via the em algorithm. Journal of the Royal Statistical Society: Series B, 39(1): 1-38.
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