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Voxel-based texture analysis of the brain.

Maani R, Yang YH, Kalra S - PLoS ONE (2015)

Bottom Line: The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis.The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups.This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing Science, University of Alberta, Edmonton, Canada.

ABSTRACT
This paper presents a novel voxel-based method for texture analysis of brain images. Texture analysis is a powerful quantitative approach for analyzing voxel intensities and their interrelationships, but has been thus far limited to analyzing regions of interest. The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis. The validity of the method was examined on artificially generated effects as well as on real MRI data in Alzheimer's Disease (AD). The artificially generated effects included hyperintense and hypointense signals added to T1-weighted brain MRIs from 30 healthy subjects. The AD dataset included 30 patients with AD and 30 age/sex matched healthy control subjects. The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups. This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.

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Related in: MedlinePlus

The statistical maps of sum of squares: variance (f6) in the analysis of artificial lesions.The regions with statistically significant difference (corrected by FRD at p<0.05) are shown (i.e., detection regions). The ground truth locations of lesions are shown in S1 Fig. The types of artificial lesion are shown by roman number (i.e., I, II,…,VIII) and defined in S1 Table. The maps were computed by VGLCM-TOP-3D at quantization level of 8.
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pone.0117759.g004: The statistical maps of sum of squares: variance (f6) in the analysis of artificial lesions.The regions with statistically significant difference (corrected by FRD at p<0.05) are shown (i.e., detection regions). The ground truth locations of lesions are shown in S1 Fig. The types of artificial lesion are shown by roman number (i.e., I, II,…,VIII) and defined in S1 Table. The maps were computed by VGLCM-TOP-3D at quantization level of 8.

Mentions: The statistical maps of f6 for the 8 different artificial effects computed by VGLCM-TOP-3D at quantization level of 8 (Fig. 4) demonstrate correct detection using the proposed methods.


Voxel-based texture analysis of the brain.

Maani R, Yang YH, Kalra S - PLoS ONE (2015)

The statistical maps of sum of squares: variance (f6) in the analysis of artificial lesions.The regions with statistically significant difference (corrected by FRD at p<0.05) are shown (i.e., detection regions). The ground truth locations of lesions are shown in S1 Fig. The types of artificial lesion are shown by roman number (i.e., I, II,…,VIII) and defined in S1 Table. The maps were computed by VGLCM-TOP-3D at quantization level of 8.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4355627&req=5

pone.0117759.g004: The statistical maps of sum of squares: variance (f6) in the analysis of artificial lesions.The regions with statistically significant difference (corrected by FRD at p<0.05) are shown (i.e., detection regions). The ground truth locations of lesions are shown in S1 Fig. The types of artificial lesion are shown by roman number (i.e., I, II,…,VIII) and defined in S1 Table. The maps were computed by VGLCM-TOP-3D at quantization level of 8.
Mentions: The statistical maps of f6 for the 8 different artificial effects computed by VGLCM-TOP-3D at quantization level of 8 (Fig. 4) demonstrate correct detection using the proposed methods.

Bottom Line: The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis.The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups.This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing Science, University of Alberta, Edmonton, Canada.

ABSTRACT
This paper presents a novel voxel-based method for texture analysis of brain images. Texture analysis is a powerful quantitative approach for analyzing voxel intensities and their interrelationships, but has been thus far limited to analyzing regions of interest. The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis. The validity of the method was examined on artificially generated effects as well as on real MRI data in Alzheimer's Disease (AD). The artificially generated effects included hyperintense and hypointense signals added to T1-weighted brain MRIs from 30 healthy subjects. The AD dataset included 30 patients with AD and 30 age/sex matched healthy control subjects. The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups. This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.

Show MeSH
Related in: MedlinePlus