An artificial immune-activated neural network applied to brain 3D MRI segmentation

Akmal Younis, Mohamed Ibrahim, Mansur Kabuka, Nigel John

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

In this paper, a new neural network model inspired by the biological immune system functions is presented. The model, termed Artificial Immune-Activated Neural Network (AIANN), extracts classification knowledge from a training data set, which is then used to classify input patterns or vectors. The AIANN is based on a neuron activation function whose behavior is conceptually modeled after the chemical bonds between the receptors and epitopes in the biological immune system. The bonding is controlled through an energy measure to ensure accurate recognition. The AIANN model was applied to the segmentation of 3-dimensional magnetic resonance imaging (MRI) data of the brain and a contextual basis was developed for the segmentation problem. Evaluation of the segmentation results was performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results demonstrated that the AIANN model attained higher average results than those obtained using published methods for real MRI data and simulated MRI data, especially at low levels of noise.

Original languageEnglish (US)
Pages (from-to)S69-S88
JournalJournal of Digital Imaging
Volume21
Issue numberSUPPL. 1
DOIs
StatePublished - Oct 2008

Keywords

  • Artificial immune systems
  • Brain segmentation
  • Intensity level correction
  • MRI
  • Neural networks

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Computer Science Applications

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