Possibilistic Hopfield Neural Network on CT Brain Hemorrhage Image Segmentation

In this paper, a possibilistic Hopfield neural netw ork (PHNN) has been proposed for clustering and subsequ ently applied to brain hemorrhage image segmentation base d on a series of CT images. The neural network structure has been implemented by imbedding the weighting possibi li tic c-means algorithm into a Hopfield neural network. T he network solved the coincidental cluster problem by using a weighting factor and it can also be implemented in parallel. The proposed neural network has been compared to fu zzy c-means (FCM), possibilistic c-means (PCM), and fuz zypossibilistic c-means (FPCM) algorithms by using bo th simulated data and real images. The results showed that PHNN was more noise-resistant and reliable than the old ones.

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