A new method to trace colloid transport pathways in macroporous soils using X‐ray computed tomography and fluorescence macrophotography

The fast and deep percolation of particles through soil is attributed to preferential flow pathways, and their extent can be critical in the filtering of particulate pollutants in soil. Particle deposition on the pore walls and transport between the pores and matrix modulate the preferential flow of particulate pollutants. In the present research, we developed a novel method of combining fluorescence macrophotography and X‐ray computed tomography (CT) to track preferential pathways of colloidal fluorescent microspheres (MS) in breakthrough experiments. We located accumulations of MS by fluorescence imaging and used them to delimit the deposition structures along the preferential colloid pathways by superimposing these images on the 3‐D pore network obtained from CT. Advection–diffusion with transport parameters from the dual‐porosity equation correlated with preferential pathway features across different soil management techniques. However, management did not influence the morphology of the MS preferential pathways. Preferential flow occurred in only a small fraction of the total pore network and was controlled by pores connected to the soil surface and by matrix density

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

In Llanas and Lantarón, J. Sci. Comput. 46, 485–518 (2011) we proposed an algorithm (EDAS-d) to approximate the jump discontinuity set of functions defined on subsets of ℝ d . This procedure is based on adaptive splitting of the domain of the function guided by the value of an average integral. The above study was limited to the 1D and 2D versions of the algorithm. In this paper we address the three-dimensional problem. We prove an integral inequality (in the case d=3) which constitutes the basis of EDAS-3. We have performed detailed computational experiments demonstrating effective edge detection in 3D function models with different interface topologies. EDAS-1 and EDAS-2 appealing properties are extensible to the 3D cas

An Automatic Rib Segmentation Method on X-Ray Radiographs

In this paper, an automatic rib recognition method based on image processing and data mining is presented. Firstly, multiple template matching and graph based methods are used to detect rib center line; then, the support vector machine is used to build a rib relative position model and identify the error recognition results; finally, decision trees are employed to refine the center line recognition result. The JSRT database is employed to test our method. The result of rib recognition is over 92% for sensitivity and 98% for specificity