Comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront

Handle URI:
http://hdl.handle.net/10754/626682
Title:
Comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront
Authors:
Tamamitsu, Miu; Zhang, Yibo; Wang, Hongda; Wu, Yichen; Ozcan, Aydogan
Abstract:
The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsity metric is applied. Here, we compare two different choices of sparsity metrics used in SoG, specifically, the Gini index (GI) and the Tamura coefficient (TC), for holographic autofocusing on dense/connected or sparse samples. We provide a theoretical analysis predicting that for uniformly distributed image data, TC and GI exhibit similar behavior, while for naturally sparse images containing few high-valued signal entries and many low-valued noisy background pixels, TC is more sensitive to distribution changes in the signal and more resistive to background noise. These predictions are also confirmed by experimental results using SoG-based holographic autofocusing on dense and connected samples (such as stained breast tissue sections) as well as highly sparse samples (such as isolated Giardia lamblia cysts). Through these experiments, we found that ToG and GoG offer almost identical autofocusing performance on dense and connected samples, whereas for naturally sparse samples, GoG should be calculated on a relatively small region of interest (ROI) closely surrounding the object, while ToG offers more flexibility in choosing a larger ROI containing more background pixels.
Publisher:
arXiv
Issue Date:
27-Aug-2017
ARXIV:
arXiv:1708.08055
Type:
Preprint
Appears in Collections:
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Full metadata record

DC FieldValue Language
dc.contributor.authorTamamitsu, Miuen
dc.contributor.authorZhang, Yiboen
dc.contributor.authorWang, Hongdaen
dc.contributor.authorWu, Yichenen
dc.contributor.authorOzcan, Aydoganen
dc.date.accessioned2018-01-04T07:51:39Z-
dc.date.available2018-01-04T07:51:39Z-
dc.date.issued2017-08-27en
dc.identifier.urihttp://hdl.handle.net/10754/626682-
dc.description.abstractThe Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsity metric is applied. Here, we compare two different choices of sparsity metrics used in SoG, specifically, the Gini index (GI) and the Tamura coefficient (TC), for holographic autofocusing on dense/connected or sparse samples. We provide a theoretical analysis predicting that for uniformly distributed image data, TC and GI exhibit similar behavior, while for naturally sparse images containing few high-valued signal entries and many low-valued noisy background pixels, TC is more sensitive to distribution changes in the signal and more resistive to background noise. These predictions are also confirmed by experimental results using SoG-based holographic autofocusing on dense and connected samples (such as stained breast tissue sections) as well as highly sparse samples (such as isolated Giardia lamblia cysts). Through these experiments, we found that ToG and GoG offer almost identical autofocusing performance on dense and connected samples, whereas for naturally sparse samples, GoG should be calculated on a relatively small region of interest (ROI) closely surrounding the object, while ToG offers more flexibility in choosing a larger ROI containing more background pixels.en
dc.publisherarXiven
dc.titleComparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefronten
dc.typePreprinten
dc.contributor.institutionCalifornia NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.en
dc.contributor.institutionBioengineering Department, University of California, Los Angeles, CA, 90095, USA.en
dc.contributor.institutionElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.en
dc.contributor.institutionDepartment of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.en
dc.identifier.arxividarXiv:1708.08055en
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