Possibilistic Hopfield Neural Network on CT Brain Hemorrhage Image Segmentation
نویسنده
چکیده
In this paper, a possibilistic Hopfield neural network (PHNN) has been proposed for clustering and subsequently applied to brain hemorrhage image segmentation based on a series of CT images. The neural network structure has been implemented by imbedding the weighting possibilistic c-means algorithm into a Hopfield neural network. The 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 fuzzy c-means (FCM), possibilistic c-means (PCM), and fuzzypossibilistic c-means (FPCM) algorithms by using both simulated data and real images. The results showed that PHNN was more noise-resistant and reliable than the old ones.
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