Automatic ROI selection in structural brain MRI using SOM 3D projection

Andrés Ortiz; Juan M Górriz; Javier Ramírez; Francisco J Martinez-Murcia; Alzheimer's Disease Neuroimaging Initiative

doi:10.1371/journal.pone.0093851

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Automatic ROI selection in structural brain MRI using SOM 3D projection

Journal article

Open access

Peer reviewed

Automatic ROI selection in structural brain MRI using SOM 3D projection

Andrés Ortiz, Juan M Górriz, Javier Ramírez, Francisco J Martinez-Murcia and Alzheimer's Disease Neuroimaging Initiative

PloS one, Vol.9(4), pp.e93851-e93851

2014

DOI: 10.1371/journal.pone.0093851

PMCID: PMC3984096

PMID: 24728041

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Abstract

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.

Algorithms

Humans

Aged, 80 and over

Brain - pathology

Magnetic Resonance Imaging - methods

Female

Male

Aged

Alzheimer Disease - pathology

Details

Title: Subtitle: Automatic ROI selection in structural brain MRI using SOM 3D projection
Creators: Andrés Ortiz - Communications Engineering Department, Universidad de Málaga, Málaga, Spain
Juan M Górriz - Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
Javier Ramírez - Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
Francisco J Martinez-Murcia - Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
Alzheimer's Disease Neuroimaging Initiative
Contributors: Susan K Schultz (Contributor) - University of Iowa, Internal Medicine
Resource Type: Journal article
Publication Details: PloS one, Vol.9(4), pp.e93851-e93851
DOI: 10.1371/journal.pone.0093851
PMID: 24728041
PMCID: PMC3984096
NLM abbreviation: PLoS One
ISSN: 1932-6203
eISSN: 1932-6203
Publisher: United States
Grant note: Canadian Institutes of Health Research UL1 TR001108 / NCATS NIH HHS U19 AG010483 / NIA NIH HHS P30 AG013846 / NIA NIH HHS P41 EB015922 / NIBIB NIH HHS U01 AG024904 / NIA NIH HHS UL1 TR002529 / NCATS NIH HHS
Language: English
Date published: 2014
Academic Unit: Psychiatry; Internal Medicine
Record Identifier: 9983931737902771

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