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

Akmal Younis, Mohamed Ibrahim, Mansur R. Kabuka, Nigel John

Research output: Contribution to journalArticle

17 Citations (Scopus)

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
JournalJournal of Digital Imaging
Volume21
Issue numberSUPPL. 1
DOIs
StatePublished - Oct 1 2008

Fingerprint

Magnetic resonance
Brain
Magnetic Resonance Imaging
Neural networks
Imaging techniques
Neural Networks (Computer)
Immune system
Immune System
Epitopes
Chemical bonds
General Hospitals
Neurons
Noise
Simulators
Chemical activation

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

Cite this

An artificial immune-activated neural network applied to brain 3D MRI segmentation. / Younis, Akmal; Ibrahim, Mohamed; Kabuka, Mansur R.; John, Nigel.

In: Journal of Digital Imaging, Vol. 21, No. SUPPL. 1, 01.10.2008.

Research output: Contribution to journalArticle

Younis, Akmal ; Ibrahim, Mohamed ; Kabuka, Mansur R. ; John, Nigel. / An artificial immune-activated neural network applied to brain 3D MRI segmentation. In: Journal of Digital Imaging. 2008 ; Vol. 21, No. SUPPL. 1.
@article{4ff0b5166db4473c974a0c7d93257d19,
title = "An artificial immune-activated neural network applied to brain 3D MRI segmentation",
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.",
keywords = "Artificial immune systems, Brain segmentation, Intensity level correction, MRI, Neural networks",
author = "Akmal Younis and Mohamed Ibrahim and Kabuka, {Mansur R.} and Nigel John",
year = "2008",
month = "10",
day = "1",
doi = "10.1007/s10278-007-9081-0",
language = "English",
volume = "21",
journal = "Journal of Digital Imaging",
issn = "0897-1889",
publisher = "Springer New York",
number = "SUPPL. 1",

}

TY - JOUR

T1 - An artificial immune-activated neural network applied to brain 3D MRI segmentation

AU - Younis, Akmal

AU - Ibrahim, Mohamed

AU - Kabuka, Mansur R.

AU - John, Nigel

PY - 2008/10/1

Y1 - 2008/10/1

N2 - 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.

AB - 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.

KW - Artificial immune systems

KW - Brain segmentation

KW - Intensity level correction

KW - MRI

KW - Neural networks

UR - http://www.scopus.com/inward/record.url?scp=54949158794&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=54949158794&partnerID=8YFLogxK

U2 - 10.1007/s10278-007-9081-0

DO - 10.1007/s10278-007-9081-0

M3 - Article

C2 - 18071820

AN - SCOPUS:54949158794

VL - 21

JO - Journal of Digital Imaging

JF - Journal of Digital Imaging

SN - 0897-1889

IS - SUPPL. 1

ER -