Segmentation of dual-echo MR images using neural networks

We have integrated Kohonen's self-organizing feature maps with the idea of fuzzy sets and applied this model to the problem of dual-echo MR image segmentation. In the proposed method, a Kohonen network provides the basic structure and update rule, whereas fuzzy membership values control the learning rate. The calculation of learning rate is based on a fuzzy clustering algorithm. In the experiments, spatially registered T2-weighted and proton density MR data are used as input images. Every input image is first converted to a 1-D vector and two such vectors from two images are then combined to form a 2-D matrix. The initial weights are then fed into the model to start the iterative process. The process terminates when the stopping criteria is met. The major strength of the proposed approach is its stability and unsupervised nature. The experimental results show that the speed of convergence is faster than that of the fuzzy clustering method and the conventional region-based segmentation methods.