Background: Expectation maximizing (EM) is one of the common approaches for image segmentation.
Methods: an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.
Results: the findings show that the proposed algorithm produces higher similarity index.
Conclusions: experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.