Abstract
A new method based on the competitive layer model (CLM) implemented by Lotka-Volterra recurrent neural networks (LV RNNs) is proposed for brain MR image segmentation. This method firstly divides an MR image into sub-images, and segments each sub-image by the CLM of the LV RNN to obtain a lot of 4-connected regions. Secondly, any two neighboring regions that are similar to each other are merged to form one region. Finally, all remaining regions are clustered by the RFCM into background, CSF, GM and WM. Compared with other three methods using numerical simulations, our method is shown to be more effective.
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