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Updated: Dec 15, 2025
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Brain Tumor Segmentation of T1w MRI Images Based on Clustering Using Dimensionality Reduction Random Projection
K Rajesh Babu1, P V Nagajaneyulu2, K Satya Prasad3
1Department of Electronics and Communication Engineering, Faculty of KL University, Guntur, India.
Background:
Early diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. The availability of high-dimensional medical image data during diagnosis places a heavy computational burden and a suitable pre-processing step is required for lower- dimensional representation. The storage requirement and complexity of image data are also a concern. To address this concern, the random projection technique (RPT) is widely used as a multivariate approach for data reduction.
Aim:
This study mainly focuses on T1-weighted MRI image clustering for brain tumor segmentation with dimension reduction by using the conventional principal component analysis (PCA) and RPT.
Methods:
Two clustering algorithms, K-means and fuzzy c-means (FCM) were used for brain tumor detection. The primary study objective was to present a comparison of the two clustering methods between MRI images subjected to PCA and RPT. In addition to the original dimension of 512 × 512, three other image sizes, 256 × 256, 128 × 128, and 64 × 64, were used to determine the effect of the methods.
Results:
In terms of average reconstruction, Euclidean distance, and segmentation distance errors, the RPT produced better results than the PCA method for all the clustered images from clustering techniques.
Conclusion:
According to the values of performance metrics, RPT supported fuzzy c-means in achieving the best clustering performance and provided significant results for each new size of the MRI images.
