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.
David Joon Ho, Paul Salama, Kenneth W. Dunn, and Edward J. Delp, "Boundary segmentation for fluorescence microscopy using steerable filters," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330E (Presented at SPIE Medical Imaging: February 12, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254627.
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