+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

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



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



Computerized Medical Imaging and Graphics 34(4): 308-320



We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM-PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM-PCNN is compared with that of the non-adaptive EM-PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM+WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM-PCNN significantly outperforms the non-adaptive EM-PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM-PCNN significantly outperforms the BCFCM only. However, the adaptive EM-PCNN is better than the non-adaptive EM-PCNN and EM on average. We conclude that of the three approaches, the adaptive EM-PCNN yields the best results for gray matter and brain parenchyma segmentation.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 053684094

Download citation: RISBibTeXText

PMID: 20042313

DOI: 10.1016/j.compmedimag.2009.12.002


Related references

Perfect image segmentation using pulse coupled neural networks. IEEE Transactions on Neural Networks 10(3): 591-598, 1999

Automatic segmentation of vertebrae in 3D CT images using adaptive fast 3D pulse coupled neural networks. Australasian Physical and Engineering Sciences in Medicine 41(4): 1009-1020, 2018

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging. Frontiers in Neuroscience 13: 285, 2019

Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artificial Intelligence in Medicine 95: 64-81, 2019

Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. Journal of Medical Imaging 6(2): 024005, 2019

Image binary segmentation based on pulse-coupled neural network for the locust detection system. 2007

Neural network segmentation of magnetic resonance spin echo image of the brain. Journal of Biomedical Engineering 15(5): 355-362, 1993

Segmentation of magnetic resonance brain images using analogue constraint satisfaction neural networks. Image and Vision Computing 12(6): 345-354, 1994

Optimal imaging parameters and the advantage of cerebrospinal fluid flow image using time-spatial labeling inversion pulse at 3 tesla magnetic resonance imaging: comparison of image quality for 1.5 tesla magnetic resonance imaging. Nihon Hoshasen Gijutsu Gakkai Zasshi 70(12): 1439-1444, 2014

Optimal Imaging Parameters and the Advantage of Renal Artery Image Using Time-spatial Labeling Inversion Pulse at 3 Tesla Magnetic Resonance Imaging: Comparison of Image Quality for 1.5 Tesla Magnetic Resonance Imaging. Nihon Hoshasen Gijutsu Gakkai Zasshi 72(11): 1113-1121, 2016

Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks. IEEE Transactions on Medical Imaging 16(6): 911-918, 1997

Multi-focus image fusion algorithm based on pulse coupled neural networks and modified decision map. Optik - International Journal for Light and Electron Optics 157: 1003-1015, 2018

Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks. Magnetic Resonance Imaging 21(8): 901-912, 2003

Image fusion method based on adaptive pulse coupled neural network in the discrete fractional random transform domain. Optik - International Journal for Light and Electron Optics 126(23): 3644-3651, 2015

Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging. Journal of Magnetic Resonance Imaging 40(6): 1414-1421, 2014