IEEE Trans Image Process - 3D superalloy grain segmentation using a multichannel edge-weighted centroidal Voronoi tessellation algorithm.

Tópicos

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Resumo

Accurate grain segmentation on 3D superalloy images is very important in materials science and engineering. From grain segmentation, we can derive the underlying superalloy grains' micro-structures, based on how many important physical, mechanical, and chemical properties of the superalloy samples can be evaluated. Grain segmentation is, however, usually a very challenging problem because: 1) even a small 3D superalloy sample may contain hundreds of grains; 2) carbides and noises may degrade the imaging quality; and 3) the intensity within a grain may not be homogeneous. In addition, the same grain may present different appearances, e.g., different intensities, under different microscope settings. In practice, a 3D superalloy image may contain multichannel information where each channel corresponds to a specific microscope setting. In this paper, we develop a multichannel edge-weighted centroidal Voronoi tessellation (MCEWCVT) algorithm to effectively and robustly segment the superalloy grains from 3D multichannel superalloy images. MCEWCVT performs segmentation by minimizing an energy function, which encodes both the multichannel voxel-intensity similarity within each cluster in the intensity domain and the smoothness of segmentation boundaries in the 3D image domain. In the experiment, we first quantitatively evaluate the proposed MCEWCVT algorithm on a four-channel Ni-based 3D superalloy data set (IN100) against the manually annotated ground-truth segmentation. We further evaluate the MCEWCVT algorithm on two synthesized four-channel superalloy data sets. The qualitative and quantitative comparisons of 18 existing image segmentation algorithms demonstrate the effectiveness and robustness of the proposed MCEWCVT algorithm.

Resumo Limpo

accur grain segment d superalloy imag import materi scienc engin grain segment can deriv under superalloy grain microstructur base mani import physic mechan chemic properti superalloy sampl can evalu grain segment howev usual challeng problem even small d superalloy sampl may contain hundr grain carbid nois may degrad imag qualiti intens within grain may homogen addit grain may present differ appear eg differ intens differ microscop set practic d superalloy imag may contain multichannel inform channel correspond specif microscop set paper develop multichannel edgeweight centroid voronoi tessel mcewcvt algorithm effect robust segment superalloy grain d multichannel superalloy imag mcewcvt perform segment minim energi function encod multichannel voxelintens similar within cluster intens domain smooth segment boundari d imag domain experi first quantit evalu propos mcewcvt algorithm fourchannel nibas d superalloy data set manual annot groundtruth segment evalu mcewcvt algorithm two synthes fourchannel superalloy data set qualit quantit comparison exist imag segment algorithm demonstr effect robust propos mcewcvt algorithm

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