Spectral clustering as a diagnostic tool in cross-sectional MR studies: an application to mild dementia.
Structural imaging investigations commonly apply a segmentation step followed by the extraction of feature data that can be used to compare or discriminate groups. We present a framework for such a study based on automated multi-atlas segmentation followed by the extraction of low-level morphological features, volumes and overlaps, for classification. A spectral analysis step is used to transform pairwise overlap information into feature data that relate to individual subjects. Applying the framework to a group of controls and patients with mild dementia, we compare the volume- and overlap-based classification performance using both supervised and unsupervised classifiers. The results indicate that unsupervised classification following a spectral analysis of label overlaps performs very well, outperforming classifiers that use volumes alone.
Aljabar
Paul
P
Department of Computing, Imperial College London, UK.
Rueckert
Daniel
D
Crum
William R
WR
D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D003704:Dementia / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique