Segmentation Algorithm for MRI Images Using Global Entropy Minimization

Medical image processing plays an important role in supporting the diagnosis of various diseases. Brain magnetic resonance imaging (MRI) image is widely used to support the decisions from doctors who will decide if there are any issues in a brain. The essence of the MRI is segmentation which is the basic for damaged area selection, quantitative measurement and 3-dimensional reconstruction. In order to effectively identify the located objects, this paper introduces a segmentation algorithm using global entropy minimization. This algorithm uses two times segmentation approach based on the cluster area image model to overcome the negative influences of shifted segmentation. From the experiments, the proposed algorithm get the best performance and keeps the highest accuracy. For the similarity, the proposed algorithm has almost the same performance of least biased fuzzy clustering (LBFC) which have 10% outperformance on fuzzy C-means algorithm (FCMA).