diff --git a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/4_noise_removal.py b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/4_noise_removal.py
index 85ca5c5a..f7ba3c9c 100644
--- a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/4_noise_removal.py	
+++ b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/4_noise_removal.py	
@@ -5,7 +5,7 @@ import cv2
 image = cv2.imread("./output/otsu.jpg", cv2.IMREAD_GRAYSCALE)
 
 # try several different sizes of structuring element (must be odd)
-for kernel_size in range(1, 16, 2):
+for kernel_size in range(1, 32, 2):
     # define a disk-shaped structuring element
     structuring_element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
 
diff --git a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/5_extraction_of_binary_region_of_interest.py b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/5_extraction_of_binary_region_of_interest.py
new file mode 100644
index 00000000..8bbaaac8
--- /dev/null
+++ b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/5_extraction_of_binary_region_of_interest.py	
@@ -0,0 +1,23 @@
+# Task 1.5: Extraction of Binary Regions of Interest / Connected Components
+import cv2
+import numpy as np
+
+# read in noise-reduced image
+image = cv2.imread("./output/kernel_size_25.jpg", cv2.IMREAD_GRAYSCALE)
+
+# Find connected components
+num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)
+
+# Create an output image (color) to label components
+output_img = np.zeros((image.shape[0], image.shape[1], 3), dtype=np.uint8)
+
+# Apply a single color (e.g., gray) to each component in the output image
+for label in range(1, num_labels):  # Skip background (label 0)
+    output_img[labels == label] = (200, 200, 200)  # Light gray color for each component
+
+# Overlay red text labels at component centroids
+for i in range(1, num_labels):  # Skip background (label 0)
+    x, y = int(centroids[i][0]), int(centroids[i][1])
+    cv2.putText(output_img, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)  # Red color (BGR: (0, 0, 255))
+
+cv2.imwrite("./output/region_of_interest.jpg", output_img)
diff --git a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/output/kernel_size_17.jpg b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/code/task1/output/kernel_size_17.jpg
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diff --git a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.pdf b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.pdf
index 0d45725d..ff125532 100644
Binary files a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.pdf and b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.pdf differ
diff --git a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex
index bfa9163e..ae80544b 100644
--- a/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex	
+++ b/year4/semester1/CT404: Graphics & Image Processing/assignments/assignment2/latex/CT404-Assignment-2.tex	
@@ -180,6 +180,7 @@ As can be seen from the above output, the optimal value chosen was 129.
     \caption{Image with Otsu thresholding}
 \end{figure}
 
+
 \subsection{Noise Removal}
 \begin{code}
     \inputminted[linenos, breaklines, frame=single]{python}{../code/task1/4_noise_removal.py}
@@ -191,6 +192,10 @@ As can be seen from the above output, the optimal value chosen was 129.
     \centering
     \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_1.jpg}
     \captionof{figure}{\mintinline{python}{kernel_size = 1}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
     \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_3.jpg}
     \captionof{figure}{\mintinline{python}{kernel_size = 3}}
 \end{minipage}
@@ -199,14 +204,22 @@ As can be seen from the above output, the optimal value chosen was 129.
     \centering
     \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_5.jpg}
     \captionof{figure}{\mintinline{python}{kernel_size = 5}}
-    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_7.jpg}
-    \captionof{figure}{\mintinline{python}{kernel_size = 7}}
 \end{minipage}
 \hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_7.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 7}}
+\end{minipage}
+
 \begin{minipage}{0.24\textwidth}
     \centering
     \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_9.jpg}
     \captionof{figure}{\mintinline{python}{kernel_size = 9}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
     \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_11.jpg}
     \captionof{figure}{\mintinline{python}{kernel_size = 11}}
 \end{minipage}
@@ -215,8 +228,73 @@ As can be seen from the above output, the optimal value chosen was 129.
     \centering
     \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_13.jpg}
     \captionof{figure}{\mintinline{python}{kernel_size = 13}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
     \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_15.jpg}
     \captionof{figure}{\mintinline{python}{kernel_size = 15}}
 \end{minipage}
 
+\noindent
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_17.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 17}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_19.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 19}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_21.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 21}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_23.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 23}}
+\end{minipage}
+
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_25.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 25}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_27.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 27}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_29.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 29}}
+\end{minipage}
+\hfill
+\begin{minipage}{0.24\textwidth}
+    \centering
+    \includegraphics[width=\textwidth]{../code/task1/output/kernel_size_31.jpg}
+    \captionof{figure}{\mintinline{python}{kernel_size = 31}}
+\end{minipage}
+
+I chose to go with \mintinline{python}{kernel_size = 25} as it seemed to give the optimal balance between removing noise without significantly reducing the size of the remaining fat globules .
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.5\textwidth]{../code/task1/output/kernel_size_25.jpg}
+    \caption{Chosen noise threshold: \mintinline{python}{kernel_size = 25}}
+\end{figure}
+
+\subsection{Extraction of Binary Regions of Interest / Connected Components}
+
+
+
 \end{document}