A histogram-based segmentation method for characterization of self-assembled hexagonal lattices

Lattice characterization techniques are often used to quantify the effects of different anodization conditions on nano-porous anodized aluminum oxides. In this work, we develop a comprehensive hexagonal lattice characterization method to evaluate the amount of ordering of the lattice and localize the domains of the image and report their characteristics. A robust preprocessing is proposed to find pores' centroids. Different domains of SEM images usually have different orientations. Pores orientation distribution is analyzed using angle-histogram. The valleys of angle-histogram are employed as thresholds to separate different dominant orientations. We show that using orientation as a distinguishing feature of different domains, significantly improves the robustness of the algorithm against tolerance parameters. Some new parameters are introduced to exactly characterize each of the domains and the whole lattice.

► A new robust method to localize the domains of nano-porous alumina images is introduced. ► A three step robust preprocessing technique is employed to convert the original image into a binary one. ► A new distinguishing criterion, orientation, is employed, to segment the image into its domains, which makes the segmentation part of our algorithm significantly robust against ordering tolerance parameters. ► A new concept of angle-histogram is proposed in this paper. ► Angle-histogram analysis enables us to segment the image into domains in an unsupervised way.