Improved 3D image segmentation for X-ray tomographic analysis of packed particle beds

Abstract Although High Resolution X-ray Micro Tomography (HRXMT) has been developed in the past years for the 3D analysis of multiphase mineral particles in packed particle beds, image analysis of fine and/or high-density/high atomic number particles has been limited by existing segmentation algorithms. In this regard, a feature-based segmentation algorithm has been developed and demonstrated to provide a more accurate image processing method for the analysis of such multiphase particle populations. Based on this improved segmentation algorithm, image analyses of packed particle bed samples were compared to segmentation by traditional 3D watershed segmentation. Also, calculation of particle number using optical microscopy, together with a digital camera, was accomplished to validate feature-based segmentation. Detailed procedures and results for sample preparation, image analysis and validation are presented and discussed.

[1]  Joachim M. Buhmann,et al.  Neuron geometry extraction by perceptual grouping in ssTEM images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Jan D. Miller,et al.  Three-dimensional analysis of particulates in mineral processing systems by cone beam X-ray microtomography , 2004 .

[3]  A. R. Videla,et al.  Watershed Functions Applied to a 3D Image Segmentation Problem for the Analysis of Packed Particle Beds , 2006 .

[4]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[5]  Jan D. Miller,et al.  Liberation-limited grade/recovery curves from X-ray micro CT analysis of feed material for the evaluation of separation efficiency , 2009 .

[6]  Jan D. Miller,et al.  Recent advances in the application of X-ray computed tomography in the analysis of heap leaching systems , 2012 .

[7]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  H. Sebastian Seung,et al.  Trainable Weka Segmentation: A Machine Learning Tool for Microscopy Image Segmentation , 2014 .

[9]  Jan D. Miller,et al.  Evaluation of pyrite flotation efficiency using liberation-limited grade/recovery curves , 2012 .

[10]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[11]  N J Pelc,et al.  Nonlinear partial volume artifacts in x-ray computed tomography. , 1980, Medical physics.

[12]  C. L. Lin,et al.  Characterization and analysis of Porous, Brittle solid structures by X-ray micro computed tomography , 2010 .

[13]  Jan D. Miller,et al.  3D characterization and analysis of particle shape using X-ray microtomography (XMT) , 2005 .

[14]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .