A hybrid Harmony Search algorithm to MRI brain segmentation
Osama Moh’d Alia, Rajeswari Mandava, Mohd Ezane Aziz · 2010
Automatic brain MRI image segmentation is a challenging problem and received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the Harmony Search (HS) hybridized with Fuzzy C-means called DCHS to automatically segment the brain MRI image in an intelligent manner. In this algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length in each harmony memory vector, DCHS is able to encode variable numbers of candidate cluster centers at each iteration. Furthermore, a new HS operator, called the `empty operator' is introduced to support the selection of empty decision variables in the harmony memory vector. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. The proposed algorithm is applied on several simulated T1-weighted normal and MS lesion magnetic resonance brain images. The experimental results show the ability of DCHS to find the appropriate number of naturally occurring regions in brain images. Furthermore, superiority of the proposed algorithm over different clustering-based algorithms is demonstrated quantitatively. All the segmented results obtained by DCHS are also compared with the available ground truth images.