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Possibilistic Hopfield Neural Network on CT Brain Hemorrhage Image Segmentation

Summary: Possibilistic Hopfield Neural Network on CT Brain Hemorrhage Image
Da­chuan Cheng, *Qin Pu, Kuo­sheng Cheng, and **Hans Burkhardt
Institute of Biomedical Engineering, National Cheng Kung University,
Tainan, Taiwan, ROC.
*Lab. B­I, Internal Medicine, Freiburg University Hospital, 79110, Freiburg, Germany.
**Informatik Institut, Freiburg Universität, 79110, Freiburg, Germany.
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
c­means 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
c­means (FCM), possibilistic c­means (PCM), and fuzzy­
possibilistic c­means (FPCM) algorithms by using both
simulated data and real images. The results showed that


Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung


Collections: Computer Technologies and Information Sciences