Voxel-Based Texture Analysis of the Brain

• Published in: PloS one Vol. 10; no. 3; p. e0117759
• Main Authors: Maani, Rouzbeh, Yang, Yee Hong, Kalra, Sanjay
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.03.2015; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - pathology; Alzheimer's disease; Analysis; Brain; Brain - pathology; Care and treatment; Case-Control Studies; Cognitive ability; Datasets; Dementia; Diagnosis; Disease; Female; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Methods; Neurodegenerative diseases; Neurological diseases; Reproducibility of Results; Statistical analysis; Statistics; Texture; Canada
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0117759

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.