Abstract:
This study investigates the application of Hierarchical clustering
for image segmentation, with a focus on its efficacy in analyzing
medical images, particularly MRI scans of brain tumors. Image
segmentation plays a pivotal role in computer vision, facilitating
various applications across industries. Leveraging a systematic
approach, we conduct a comprehensive review of recent literature
on machine learning algorithms for image segmentation. Subsequently, utilizing a dataset comprising MRI images with and without
tumors, we preprocess and analyze the data using the Histogram of
Oriented Gradients (HOG) technique to extract pertinent features.
These features serve as input for the Hierarchical clustering algorithm to partition the images into distinct regions of interest. For
each row of vectors, the Jensen-Shenton distance was calculated.
The resulting symmetric matrices are distances among the corresponding vectors, quantifying dissimilarity in cluster analysis. Our
findings underscore the effectiveness of Hierarchical clustering in
clustering medical images, with potential implications for advancing computational analysis in healthcare and related domains.
