Comparison of Clustering Algorithms for Segregating the Planar Sections of Brain Tumour MRIs

Clustering is a technique that involves grouping data into clusters based on the similarity of their characteristics. In images these can be visual features like color, texture, shape, intensity and so on. Unsupervised learning takes place here as the data is clustered without any prior knowledge of their labels or categories. MRI (Magnetic Resonance Imaging) tests capture medical images using magnetic fields and radio waves. These images are used for diagnostic purposes to detect and analyze the internal structure of organs and tissues in the human body. Clustering the planar section of these medical scans can help detect the various types of diseases or condition. This paper potrays a comparitive study by evaluating the efficiency of various clustering algorithms like K-Means, Agglomerative, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies). The aim is to find out the most appropriate algorithm for effectively identifying and clustering the different planar sections of Brain Tumour MRI scans.