An Optimized Clustering Approach using Tree Seed Algorithm for the Brain MRI Images Segmentation

Abstract

Clustering algorithms are widely used to segment medical images. However, these techniques are difficult to perform, especially in brain magnetic resonance images (MRI), given the complexity of the anatomical structure of brain tissue, the in-homogeneity of pixel intensity in these images, and partial volume and noise effects. This will cause the algorithm to fall into the local minima problem; for this reason, it is recommended to improve such clustering algorithms using optimization techniques to obtain better results. In this study, we have proposed a developed clustering algorithm and we optimized it using a tree seed algorithm (TSA) to segment brain MRI image. Algorithms are tested on real brain image datasets. The experimental results on simulated and real brain MRI datasets show that our proposed method has satisfactory results regarding the Davies-Bouldin index (DBI) compared to the fuzzy c-mean (FCM) algorithm.

Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm

“…It is said that the TSA performs well on this problem. Ghaouti and Meftah [17] conducted a study on brain MR images. Since the brain has a complex structure, it is said that MR images are not homogeneous.…”