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Title: Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images

Abstract

Zernike phase contrast is a useful technique for nanoscale X-ray computed tomography (CT) imaging of materials with a low X-ray absorption coefficient. It enhances the image contrast by phase shifting X-ray waves to create changes in amplitude. However, it creates artifacts that hinder the use of traditional image segmentation techniques. We propose an image restoration method that models the X-ray phase contrast optics and the three-dimensional image reconstruction method. We generate artifact-free images through an optimization problem that inverts this model. Though similar approaches have been used for Zernike phase contrast in visible light microscopy, this optimization employs an effective edge detection method tailored to handle Zernike phase contrast artifacts. We characterize this optics-based restoration method by removing the artifacts in and thresholding multiple Zernike phase contrast X-ray CT images to produce segmented results that are consistent with the physical specimens. We quantitatively evaluate and compare our method to other segmentation techniques to demonstrate its high accuracy.

Authors:
; ; ;  [1]
  1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 (United States)
Publication Date:
OSTI Identifier:
22410170
Resource Type:
Journal Article
Journal Name:
Journal of Applied Physics
Additional Journal Information:
Journal Volume: 117; Journal Issue: 18; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0021-8979
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; CAT SCANNING; COMPARATIVE EVALUATIONS; IMAGE PROCESSING; IMAGES; MICROSCOPY; NANOSTRUCTURES; OPTICS; OPTIMIZATION; PHASE SHIFT; THREE-DIMENSIONAL LATTICES; VISIBLE RADIATION; X RADIATION

Citation Formats

Kumar, Arjun S., Mandal, Pratiti, Zhang, Yongjie, and Litster, Shawn. Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images. United States: N. p., 2015. Web. doi:10.1063/1.4919835.
Kumar, Arjun S., Mandal, Pratiti, Zhang, Yongjie, & Litster, Shawn. Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images. United States. doi:10.1063/1.4919835.
Kumar, Arjun S., Mandal, Pratiti, Zhang, Yongjie, and Litster, Shawn. Thu . "Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images". United States. doi:10.1063/1.4919835.
@article{osti_22410170,
title = {Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images},
author = {Kumar, Arjun S. and Mandal, Pratiti and Zhang, Yongjie and Litster, Shawn},
abstractNote = {Zernike phase contrast is a useful technique for nanoscale X-ray computed tomography (CT) imaging of materials with a low X-ray absorption coefficient. It enhances the image contrast by phase shifting X-ray waves to create changes in amplitude. However, it creates artifacts that hinder the use of traditional image segmentation techniques. We propose an image restoration method that models the X-ray phase contrast optics and the three-dimensional image reconstruction method. We generate artifact-free images through an optimization problem that inverts this model. Though similar approaches have been used for Zernike phase contrast in visible light microscopy, this optimization employs an effective edge detection method tailored to handle Zernike phase contrast artifacts. We characterize this optics-based restoration method by removing the artifacts in and thresholding multiple Zernike phase contrast X-ray CT images to produce segmented results that are consistent with the physical specimens. We quantitatively evaluate and compare our method to other segmentation techniques to demonstrate its high accuracy.},
doi = {10.1063/1.4919835},
journal = {Journal of Applied Physics},
issn = {0021-8979},
number = 18,
volume = 117,
place = {United States},
year = {2015},
month = {5}
}