Last modified
April 3, 2001

  Seminar Abstract
Center for Data Intensive Computing



Image Thresholding by Indicator Kriging

IWe consider the problem of segmenting a digitized image consisting of two univariate populations. Assume a-priori knowledge allows incomplete assignment of voxels in the image, in the sense that a fraction of the voxels can be identified as belonging to population P0, a second fraction to P1, and the remaining fraction have no a-priori identification. Based upon estimates of the short length scale spatial covariance of the image, we have developed a method utilizing indicator kriging to complete the image segmentation.

This work was motivated by three dimensional synchrotron X-ray computed tomographic (CAT) or laser scanning confocal microscopic (LSCM) images of biphase materials, such as rock samples, which for our purposes consist of a material (object) and a void (background) phase.

Identifying the shape of the object is complicated by the partial voxel (finite volume of resolution) effect as well as by other noise due to tomographic reconstruction or data quality.

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