Performance Analysis of Clustering Algorithms in Brain Tumor Detection of MR Images
P. Tamije Selvy, Vadivel Palanisamy, T. Purusothaman · 2011
The brain is the anterior most part of the central nervous system. Along with the Spinal cord, it forms the Central Nervous System (CNS). Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic Resonance Imager (MRI) is the commonly used device for diagnosis. In MR images, the amount of data is too much for manual interpretation and analysis. Segmentation is an important process in most Medical Image Analysis. Clustering to magnetic resonance (MR) brain tumors maintains efficiency. Clustering is suitable for biomedical image segmentation as it uses unsupervised learning. This Paper analyses various clustering techniques to track tumor objects in Magnetic Resonance (MR) brain images. The input to this system is the MR image of the axial view of the human brain. The Clustering algorithms used are K-means, SOM, Hierarchical Clustering and Fuzzy C-Means Clustering. The given gray-level MR image is converted to a color space image and clustering algorithms are applied. The position of tumor objects is isolated from an MR image by using clustering algorithms The above clustering algorithms are analyzed and the performance is evaluated based on execution time and accuracy of the algorithms.