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.