Open AccessX‐ray Micro‐CT: How Soil Pore Space Description Can Be Altered by Image Processing
Author(s) -
Smet Sarah,
Plougonven Erwan,
Leonard Angélique,
Degré Aurore,
Beckers Eléonore
Publication year - 2017
Publication title -
vadose zone journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.036
H-Index - 81
ISSN - 1539-1663
DOI - 10.2136/vzj2016.06.0049
Subject(s) - segmentation , noise (video) , ground truth , reduction (mathematics) , artificial intelligence , noise reduction , scale space segmentation , image segmentation , computer science , histogram , pattern recognition (psychology) , computer vision , grayscale , soil science , environmental science , mathematics , image (mathematics) , geometry
A physically accurate conversion of the X-ray tomographic reconstructions of soil into pore networks requires a certain number of image processing steps. An important and much discussed issue in this field relates to segmentation, or distinguishing the pores from the solid, but preand post-segmentation noise reduction also affects the pore networks that are extracted. We used 15 two-dimensional simulated grayscale images to quantify the performance of three segmentation algorithms. These simulated images made ground-truth information available and a quantitative study feasible. The analyses were based on five performance indicators: misclassification error, non-region uniformity, and relative errors in porosity, conductance, and pore shape. Three levels of pre-segmentation noise reduction were tested, as well as two levels of post-segmentation noise reduction. Three segmentation methods were tested (two global and one local). For the local method, the threshold intervals were selected from two concepts: one based on the histogram shape and the other on the image visible-porosity value. The results indicate that pre-segmentation noise reduction significantly (p < 0.05) improves segmentation quality, but post-segmentation noise reduction is detrimental. The results also suggest that global and local methods perform in a similar way when noise reduction is applied. The local method, however, depends on the choice of threshold interval.