Shape identification of primary particles in potash alum aggregates using three-dimensional tomography data

The degree of agglomeration and the aggregate shape influence the quality of crystalline products and the ease of downstream processing. Studying the shape of primary particles in an aggregate can lead to a better understanding of the underlying aggregation mechanism. We present an automatic image processing procedure for identifying the shape, size, and position of each primary particle in microcomputed tomography (μCT) images of potash alum aggregates. Splitting an aggregate into primary particles is based on recombining watershed-transform regions, where concavity points are considered as indicators of correct segmentation. The shape identification algorithm uses the Hough transform to identify visible face normals and matches them to the set of face normals defined by a crystal model. In principle, the algorithm is applicable to other crystalline compounds provided that sufficient symmetry is present to determine the shape of a primary particle from its visible part.