Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging

Automatic magnetic resonance image segmentation by fuzzy intercluster hostility index based genetic algorithm: An application

“…The population size (Pop size) for all the processes is considered 100. In both automatic image segmentation methods, the maximum number of clusters (K max ) are considered as {12, 16 and 20} and the minimum number of clusters (K min ) are regarded as 2. In Chabrier's algorithm, the fixed number of clusters are considered as 6, 8 and 10, respectively.…”

“…This method is capable to derive the exact number of clusters in the human brain MRI images. The spatial pulse coupled neural network (PCNN) in combination with the statistical expectation maximization (EM) model is applied for MRI image segmentation [16]. An automatic segmentation method is presented by Chen et al [17] to segment the spinal cord and cerebrospinal fluid from MR images.…”

Medical Image Segmentation Using Artificial Neural Networks

“…In the improved PCNN, each neuron is activated only once. Finally, Fu et al (2010) proposed an automatic method that integrated the statistical exception maximization (EM) model and PCNN for MRI segmentation. In their method, the EM model does two major works: evaluation of the PCNN image segmentation; and adaptive adjustment of the PCNN parameters for optimal segmentation.…”

Brain MR Segmentation through Fuzzy Expectation Maximization and Histogram Based K-Means

“…Also, Fua and his colleagues proposed an automatic hybrid image segmentation model that integrates the statistical EM model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation [10].…”