MR brain image segmentation using muti-objective semi-supervised clustering

In this Paper, a new semi-supervised clustering technique using the concepts of multiobjective optimization is developed for proper segmentation of MR brain image in the intensity space. Multiple centers are used to encode a cluster in the form of a string. The proposed clustering technique utilizes intensity values of the brain pixels as the features. Additionally it also assumes that the actual class label information of 10% points of a particular image data set is also known. Three cluster validity indices are utilized as the objective functions, which are simultaneously optimized using AMOSA, a modern multiobjective optimization technique based on the concepts of simulated annealing. First two cluster validity indices are symmetry distance based Sym-index and Euclidean distance based I-index, which are based on unsupervised properties. Last one is a supervised information based cluster validity index, Minkowski Index. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on different simulated normal MR brain images available in different bands like T1-weighted, T2-weighted and proton density. The performance of the proposed semi-supervised clustering technique is compared with some other popular image segmentation techniques like Fuzzy C-means, Expectation Maximization, Multiobjective based MCMOClust technique, and Fuzzy-VGAPS clustering techniques.