2008
DOI: 10.1117/12.814578
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Processing of digital holograms for size measurements of microparticlese

Abstract: Digital holography has been reported as an effective tool for particle analysis. Other image-based techniques have small depth of focus allowing only 2D analysis at microscopic level. On the other hand, digital holography offers the ability to study volume samples from a single recording as reconstructions at different depths can be obtained. This paper focuses on the processing of the digital hologram that follows its recording in order to obtain particle size. We present a stepwise processing procedure with … Show more

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Cited by 7 publications
(28 citation statements)
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“…The mean particle size identified from the holographic microscopy was 81.1 µm and the standard deviation was 14.61 µm, which showed a good agreement with the particle size distribution which was measured using a scanning electron microscopy (SEM). 16 Fig. 4 (b) shows the ALD of ceramic beads.…”
Section: Microspheres On Glass Slidementioning
confidence: 97%
See 2 more Smart Citations
“…The mean particle size identified from the holographic microscopy was 81.1 µm and the standard deviation was 14.61 µm, which showed a good agreement with the particle size distribution which was measured using a scanning electron microscopy (SEM). 16 Fig. 4 (b) shows the ALD of ceramic beads.…”
Section: Microspheres On Glass Slidementioning
confidence: 97%
“…16 For the sake of completeness it is briefly described here. The hologram is reconstructed at several depths covering the volume to be examined.…”
Section: Extraction Of Particle Sizementioning
confidence: 99%
See 1 more Smart Citation
“…In the case of spherical particles, several localization algorithms have been developed [1,5]. A few studies have dealt with the development of localization algorithms for fibers.…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, imperfections such as non-uniform illumination of the hologram lowers the performance of thresholdbased segmentation. Some additional details regarding the drawbacks of threshold-based segmentation are given elsewhere [122].…”
Section: Image Segmentationmentioning
confidence: 99%