223 lines
7.9 KiB
TeX
223 lines
7.9 KiB
TeX
%! TeX program = lualatex
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\documentclass[a4paper]{article}
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% packages
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\usepackage{microtype} % Slightly tweak font spacing for aesthetics
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\usepackage[english]{babel} % Language hyphenation and typographical rules
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\usepackage[final, colorlinks = true, urlcolor = black, linkcolor = black]{hyperref}
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\usepackage{changepage} % adjust margins on the fly
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\usepackage{fontspec}
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\setmainfont{EB Garamond}
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\setmonofont[Scale=MatchLowercase]{Deja Vu Sans Mono}
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\usepackage{minted}
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\usemintedstyle{algol_nu}
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\usepackage{xcolor}
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\usepackage{pgfplots}
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\pgfplotsset{width=\textwidth,compat=1.9}
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\usepackage{caption}
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\newenvironment{code}{\captionsetup{type=listing}}{}
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\captionsetup[listing]{skip=0pt}
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\setlength{\abovecaptionskip}{5pt}
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\setlength{\belowcaptionskip}{5pt}
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\usepackage[yyyymmdd]{datetime}
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\renewcommand{\dateseparator}{--}
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\usepackage{titlesec}
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% \titleformat{\section}{\LARGE\bfseries}{}{}{}[\titlerule]
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% \titleformat{\subsection}{\Large\bfseries}{}{0em}{}
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% \titlespacing{\subsection}{0em}{-0.7em}{0em}
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%
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% \titleformat{\subsubsection}{\large\bfseries}{}{0em}{$\bullet$ }
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% \titlespacing{\subsubsection}{1em}{-0.7em}{0em}
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% margins
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\addtolength{\hoffset}{-2.25cm}
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\addtolength{\textwidth}{4.5cm}
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\addtolength{\voffset}{-3.25cm}
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\addtolength{\textheight}{5cm}
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\setlength{\parskip}{0pt}
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\setlength{\parindent}{0in}
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% \setcounter{secnumdepth}{0}
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\begin{document}
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\hrule \medskip
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\begin{minipage}{0.295\textwidth}
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\raggedright
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\footnotesize
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\begin{tabular}{@{}l l}
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Name: & Andrew Hayes \\
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Student ID: & 21321503 \\
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E-mail: & \href{mailto://a.hayes18@universityofgalway.ie}{a.hayes18@universityofgalway.ie} \\
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\end{tabular}
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\end{minipage}
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\begin{minipage}{0.4\textwidth}
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\centering
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\vspace{0.4em}
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\LARGE
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\textsc{ct404} \\
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\end{minipage}
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\begin{minipage}{0.295\textwidth}
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\raggedleft
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\today
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\end{minipage}
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\medskip\hrule
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\begin{center}
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\normalsize
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Assignment 2: Image Processing \& Analysis
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\end{center}
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\hrule
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\section{A Morphological Image Processing Pipeline for Medical Images}
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\begin{figure}[H]
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\centering
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\includegraphics[width=0.7\textwidth]{../Task1.jpg}
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\caption{Original Skin Biopsy Image}
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\end{figure}
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\subsection{Conversion to A Single-Channel Image}
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\begin{code}
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\inputminted[linenos, breaklines, frame=single]{python}{../code/task1/1_single_channel_conversion.py}
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\caption{\mintinline{python}{1_single_channel_conversion.py}}
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\end{code}
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Since the image has predominant hues of pink-purple, we would expect the green-channel-only image to be the one that yields the highest contrast, as pink \& purple colours are made up primarily by the blue \& red channels: the dominance of these channels results in little variance in intensity within these channels, and therefore green will have the highest intensity variance.
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This is proven true by the text output of the above code, where the standard deviation of the greyscale image based off the green channel alone is by far the highest:
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\begin{figure}[H]
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\centering
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\includegraphics[width=\textwidth]{./images/1_single_channel_conversion_output.png}
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\caption{Output of \mintinline{python}{1_single_channel_conversion.py}}
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\end{figure}
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\noindent
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\begin{minipage}{0.24\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../Task1.jpg}
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\captionof{figure}{Original image}
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\includegraphics[width=\textwidth]{../code/task1/output/greyscale.jpg}
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\captionof{figure}{Greyscale original}
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\end{minipage}
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\hfill
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\begin{minipage}{0.24\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/b_channel.jpg}
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\captionof{figure}{B-Channel}
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\includegraphics[width=\textwidth]{../code/task1/output/b_channel_greyscale.jpg}
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\captionof{figure}{B-Greyscale}
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\end{minipage}
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\hfill
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\begin{minipage}{0.24\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/g_channel.jpg}
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\captionof{figure}{G-Channel}
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\includegraphics[width=\textwidth]{../code/task1/output/g_channel_greyscale.jpg}
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\captionof{figure}{G-Greyscale}
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\end{minipage}
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\hfill
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\begin{minipage}{0.24\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/r_channel.jpg}
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\captionof{figure}{R-Channel}
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\includegraphics[width=\textwidth]{../code/task1/output/r_channel_greyscale.jpg}
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\captionof{figure}{R-Greyscale}
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\end{minipage}
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My selected single-channel image is the greyscale version of the green-channel-only image, as it yields the greatest contrast:
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\begin{figure}[H]
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\centering
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\includegraphics[width=0.5\textwidth]{../code/task1/output/g_channel_greyscale.jpg}
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\caption{Selected single-channel image: greyscale green-channel-only}
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\end{figure}
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\subsection{Image Enhancement}
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\begin{code}
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\inputminted[linenos, breaklines, frame=single]{python}{../code/task1/2_image_enhancement.py}
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\caption{\mintinline{python}{2_image_enhancement.py}}
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\end{code}
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\begin{figure}[H]
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\centering
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\includegraphics[width=\textwidth]{./images/2_image_enhancement_output.png}
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\caption{Output of \mintinline{python}{2_image_enhancement.py}}
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\end{figure}
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I chose to use the histogram equalisation technique as it gave the best contrast, as seen from the calculated standard deviation in contrast above and in the output images below.
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\begin{minipage}{0.49\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/histogram_equalised.jpg}
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\captionof{figure}{Histogram-equalised image}
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\end{minipage}
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\hfill
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\begin{minipage}{0.49\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/contrast_stretched.jpg}
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\captionof{figure}{Contrast-stretched image}
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\end{minipage}
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\subsection{Thresholding}
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\begin{code}
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\inputminted[linenos, breaklines, frame=single]{python}{../code/task1/3_thresholding.py}
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\caption{\mintinline{python}{3_thresholding.py}}
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\end{code}
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\begin{figure}[H]
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\centering
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\includegraphics[width=\textwidth]{./images/3_thresholding_output.png}
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\caption{Output of \mintinline{python}{3_thresholding.py}}
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\end{figure}
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I used Otsu's algorithm to find the optimal threshold value that best separated the foreground (objects of interest) from the background.
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As can be seen from the above output, the optimal value chosen was 129.
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\begin{figure}[H]
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\centering
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\includegraphics[width=0.5\textwidth]{../code/task1/output/otsu.jpg}
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\caption{Image with Otsu thresholding}
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\end{figure}
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\subsection{Noise Removal}
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\begin{code}
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\inputminted[linenos, breaklines, frame=single]{python}{../code/task1/4_noise_removal.py}
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\caption{\mintinline{python}{4_noise_removal.py}}
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\end{code}
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\noindent
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\begin{minipage}{0.24\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/kernel_size_1.jpg}
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\captionof{figure}{\mintinline{python}{kernel_size = 1}}
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\includegraphics[width=\textwidth]{../code/task1/output/kernel_size_3.jpg}
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\captionof{figure}{\mintinline{python}{kernel_size = 3}}
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\end{minipage}
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\hfill
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\begin{minipage}{0.24\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/kernel_size_5.jpg}
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\captionof{figure}{\mintinline{python}{kernel_size = 5}}
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\includegraphics[width=\textwidth]{../code/task1/output/kernel_size_7.jpg}
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\captionof{figure}{\mintinline{python}{kernel_size = 7}}
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\end{minipage}
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\hfill
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\begin{minipage}{0.24\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/kernel_size_9.jpg}
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\captionof{figure}{\mintinline{python}{kernel_size = 9}}
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\includegraphics[width=\textwidth]{../code/task1/output/kernel_size_11.jpg}
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\captionof{figure}{\mintinline{python}{kernel_size = 11}}
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\end{minipage}
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\hfill
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\begin{minipage}{0.24\textwidth}
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\centering
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\includegraphics[width=\textwidth]{../code/task1/output/kernel_size_13.jpg}
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\captionof{figure}{\mintinline{python}{kernel_size = 13}}
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\includegraphics[width=\textwidth]{../code/task1/output/kernel_size_15.jpg}
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\captionof{figure}{\mintinline{python}{kernel_size = 15}}
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\end{minipage}
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\end{document}
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