In this paper, we propose the scanning electron microscope (SEM) image blurring model and apply this model to the joint deconvolution and segmentation method which performs deconvolution and segmentation simultaneously. In the field of materials science and engineering, automated image segmentation techniques are critical and getting exact boundary shape is especially important. However, there are still some difficulty in getting good segmentation results when the images have blurring degradation. SEM images have blurring due in part to complex electron interactions during acquisition. To improve segmentation results at object boundaries, we incorporate prior knowledge of this blurring degradation into the existing EM/MPM segmentation algorithm. Experimental results are presented to demonstrate that the proposed method can be used to improve the segmentation of microscope images of materials.

Date Published: 26 February 2013
PDF: 8 pages
Proc. SPIE 8657, Computational Imaging XI, 86570G (26 February 2013); doi: 10.1117/12.2012829

Published in SPIE Proceedings Vol. 8657:
Computational Imaging XI
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)