Automated morphological analysis of magnetic resonance brain imaging using spectral analysis

R. Aljabar, D. Rueckert, W. R. Crum

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Analysis of structural neuroimaging studies often relies on volume or shape comparisons of labeled neuroanatomical structures in two or more clinical groups. Such studies have common elements involving segmentation, morphological feature extraction for comparison, and Subject and group discrimination. We combine two state-of-the-art analysis approaches, namely automated segmentation using label fusion and classification via spectral analysis to explore the relationship between the morphology of neuroanatomical structures and clinical diagnosis in dementia. We apply this framework to a cohort of normal controls and patients with mild dementia where accurate diagnosis is notoriously difficult. We compare and contrast our ability to discriminate normal and abnormal groups on the basis of structural morphology with (supervised) and without (unsupervised) knowledge of each individual's diagnosis. We test the hypothesis that morphological features resulting from Alzheimer disease processes are the strongest discriminator between groups. (c) 2008 Elsevier Inc. All rights reserved
Original languageEnglish
Pages (from-to)225 - 235
Number of pages11
JournalNeuroImage
Volume43
Issue number2
DOIs
Publication statusPublished - 1 Nov 2008

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