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{\Large
\textbf{Robust extraction of quantitative structural and textural information from histological images}
}
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\\
Q. Caudron$^{1,\ast}$,
R. Garnier$^{1}$,
K. A. Watt$^{2}$,
J. G. Pilkington$^{2}$,
J. M. Pemberton$^{2}$,
T. A. Aboellail$^3$,
B. T. Grenfell$^{1,4}$
A. L. Graham$^1$,
\\
\bf{1} Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
\\
\bf{2} University of Edinburgh
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\bf{3} Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, USA
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\bf{4} Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
\\
$\ast$ E-mail: qcaudron@princeton.edu
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\section*{Abstract}
\section*{Introduction}
Where histopathology is used
Current problems with histopathology
Advantages of automation
Our methodology
\section*{Methods}
Image capture pipeline
Image format and preprocessing
Sigmoid adjustment for contrast
Adaptive threshold on luminosity
Cleaning - Removing small objects
Measures :
GABOR - phase-insensitive
1 Gabor filter directionality
2 Gabor filter scale
3 normalised Lacunarity
4 Shannon entropy
5 Deconvolution foci count
6 Tissue / sinusoid ratio
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\section*{Results}
\section*{Discussion}
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\section*{Acknowledgements}
QC and BTG were supported by funding from the US Department of Homeland Security contract HSHQDC-12-C-00058. BTG acknowledges support from the Bill \& Melinda Gates Foundation. BTG was funded by the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health.
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\section*{Tables}
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\begin{table}[!h]
\centering
\begin{tabular}{ l c c c }
\hline \\[-0.9em]
\textbf{Locality} & Population & Birth rate & {$\mathbf{\tau}$} \\[0.1em]
\hline \\[-0.9em]
Bornholm & 47100 & 19.4 & 15 \\[0.1em]
Faroe Islands & 28200 & 29.4 & 15 \\[0.1em]
Reykjav\'{i}k & 47100 & 24.1 & 18 \\[0.1em]
Hafnarfj\"{o}r\dh{}ur & 6000 & 22.4 & 8 \\[0.1em]
Akureyri & 7000 & 22.7 & 19 \\[0.1em]
Vestmannaeyjar \hspace{0.2cm} & 3600 & 23.5 & 7 \\[0.1em]
\hline
\end{tabular}
\caption{Mean population sizes, birth rates, and sensitivity thresholds $\tau$ for each locality. Population sizes and annual birth rates per thousand are given as the mean over the study period. Thresholds were fit by maximising the correlation between the mean simulated epidemic time-series and the reported incidence data.}
\label{tableTau}
\end{table}
\section*{Figures}
\begin{figure}[!h]
\centering
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\caption{\textbf{Reported and predicted biweekly incidence for Bornholm, the Faroe Islands, and four localities in Iceland.} The observed data is in blue. For the predicted time-series, the mean value of incidence simulations is plotted as a dark red line, with 95\% confidence intervals given in light red. Bornholm~: $R^2=0.78$; Faroe Islands~: $R^2=0.55$; Reykjav\'{i}k~: $R^2=0.73$; Hafnarfj\"{o}r\dh{}ur~: $R^2=0.86$; Akureyri~: $R^2=0.80$; Vestmannaeyjar~: $R^2=0.77$.}
\label{figIncidence}
\end{figure}
\begin{figure}[!h]
\centering
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\caption{\textbf{Reporting rates and seasonalities.} Seasonality is plotted as a function of the biweek, with 95\% confidence intervals in light blue.}
\label{figSims}
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\centering
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\caption{\textbf{Predictability of epidemic sizes.} The mean predicted size of each epidemic as a function of its observed size, from ten thousand simulations. Red lines are the regression lines with the follow coefficients of determination and slopes~-- Bornholm~: $R^2=0.76$, gradient~$=1.07$; Faroe Islands~: $R^2=0.77$, gradient~$=0.60$; Reykjav\'{i}k~: $R^2=0.64$, gradient~$=0.96$; Hafnarfj\"{o}r\dh{}ur~: $R^2=0.88$, gradient~$=1.18$; Akureyri~: $R^2 = 0.49$, gradient~$=0.72$; Vestmannaeyjar~: $R^2=0.76$, gradient~$=1.23$. The green line is the zero-intercept, gradient-one line representing a one-to-one match between observation and prediction.}
\label{fig_sizes}
\end{figure}
\end{document}
Datasets
Models
