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Proceedings Paper

Boundary segmentation for fluorescence microscopy using steerable filters
Author(s): David Joon Ho; Paul Salama; Kenneth W. Dunn; Edward J. Delp
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Paper Abstract

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.

Paper Details

Date Published: 24 February 2017
PDF: 11 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330E (24 February 2017); doi: 10.1117/12.2254627
Show Author Affiliations
David Joon Ho, Purdue Univ. (United States)
Paul Salama, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Kenneth W. Dunn, Indiana Univ. (United States)
Edward J. Delp, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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