Spatial based Expectation Maximizing (EM)

<p>Abstract</p> <p>Background</p> <p>Expectation maximizing (EM) is one of the common approaches for image segmentation.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>the findings show that the proposed algorithm produces higher similarity index.</p> <p>Conclusions</p> <p>experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.</p

A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model.

International audienceThe CT uroscan consists of three to four time-spaced acquisitions of the same patient. After registration of these acquisitions, the data forms a volume in which each voxel contains a vector of elements corresponding to the information of the CT uroscan acquisitions. In this paper we will present a segmentation tool in order to differentiate the anatomical structures within the vectorial volume. Because of the partial volume effect (PVE), soft segmentation is better suited because it allows regions or classes to overlap. Gaussian mixture model is often used in statistical classifier to realize soft segmentation by getting classes probability distributions. But this model relies only on the intensity distributions, which will lead a misclassification on the boundaries and on inhomogeneous regions with noise. In order to solve this problem, a neighborhood weighted Gaussian mixture model is proposed in this paper. Expectation maximization algorithm is used as optimization method. The experiments demonstrate that the proposed method can get a better classification result and is less affected by the noise