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Updated: Apr 20, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation
S A Praylin Selva Blessy1, C Helen Sulochana2
1Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, Kanyakumari, Tamil Nadu, India.
Background:
Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities.
Objective:
To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images.
Methods:
Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering.
Results:
Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods.
Conclusions:
Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.
