A Fourier-optical approach for segmentation of ion microprobe images into tissue and non-tissue covered areas

Fourier optics provides a powerful and objective approach to handling Poissonian statistics in images. Fourier optics has been used to develop an approach to segment microbeam images into tissue and void (non-tissue) areas (e.g. villi, crypts, blood vessels and lymph canals etc.). Image segmentation is important in order to accurately measure the major element composition and thickness of biological sections. The method is based on comparing the spatial frequency of non-zero pixels in a reference void with tissue regions using a convolution method. The method automatically and conservatively segments tissue areas and uses no free-parameters. Since the method is based on spatial frequencies it can be used over a wide span of image counting statistics.

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Publication Details

Journal
Ultramicroscopy
Published
2025-12-08
DOI
https://doi.org/10.1016/j.ultramic.2025.114299
Primary Topic
Ion-surface interactions and analysis
Type
article
Field-Weighted Citation Impact
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article

A Fourier-optical approach for segmentation of ion microprobe images into tissue and non-tissue covered areas

Rattanaporn Norarat, A.J. McLaughlin, Donruedee Toyen, François Villinger et al.
Ultramicroscopy
Ion-surface interactions and analysis
article

A Fourier-optical approach for segmentation of ion microprobe images into tissue and non-tissue covered areas

Rattanaporn Norarat, A.J. McLaughlin, Donruedee Toyen, François Villinger, Amal K. Ghosh, R.J.W. Frost, Harry J. Whitlow, Deepanwita Bose
article en

Abstract

Fourier optics provides a powerful and objective approach to handling Poissonian statistics in images. Fourier optics has been used to develop an approach to segment microbeam images into tissue and void (non-tissue) areas (e.g. villi, crypts, blood vessels and lymph canals etc.). Image segmentation is important in order to accurately measure the major element composition and thickness of biological sections. The method is based on comparing the spatial frequency of non-zero pixels in a reference void with tissue regions using a convolution method. The method automatically and conservatively segments tissue areas and uses no free-parameters. Since the method is based on spatial frequencies it can be used over a wide span of image counting statistics.

UltramicroscopyVol. 281
Uppsala University (SE), Rajamangala University of Technology Lanna (TH), University of Louisiana at Lafayette (US)
Openalex Percentile: Top 60%
Ion-surface interactions and analysis
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