2024 : 4 : 14
Maryam Momeni

Maryam Momeni

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0002-2548-1619
Education: PhD.
ScopusId: 55959639000
Faculty: Engineering
Address: Arak University


Skull Segmentation in 3D Neonatal MRI using Hybrid Hopfield Neural Network
Hybrid Hopfield Neural Network; Region of Interest (ROI); Bayesian clustering, Morphological model, skull segmentation.
Researchers Mehdi Daliri ، Hamid Abrishami Moghaddam ، Maryam Momeni ، sona ghadim ، farshid Harirchi


A fully automated method for segmentation of neonatal skull in Magnetic Resonance (MR) images for source localization of electrical/magnetic encephalography (EEG/MEG) signals is proposed. Finding the source of these signals shows the origin of an abnormality. We propose a hybrid algorithm in which a Bayesian classifying framework is combined with a Hopfield Neural Network (HNN) for neonatal skull segmentation. Due to the non-homogeneity of skull intensities in MR images, local statistical parameters are used for adaptive training of Hopfield neural network based on Bayesian classifier error. The experimental results, which are obtained on high resolution T1-weighted MR images of nine neonates with gestational ages between 39 and 42 weeks, show 65% accuracy which consistently exhibits our scheme’s superiority in comparison with previous neonatal skull segmentation methods.