Brain Tumor Segmentation of T1w MRI Images Based on Clustering Using Dimensionality Reduction Random Projection Technique.

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.