Differentiation of mr brain alzheimer images using bi-planar canonical correlation based feature fusion

Alzheimer’s Disease (AD) is a progressive irreversible neurodegenerative disorder which involves the deformations in brain sub-anatomic regions. Recent studies suggest that these deformations could be characterized using bi-planar information extracted from structural Magnetic Resonance (MR) image features. However, analysis and fusion of these bi-planar features have been a challenging task in AD differentiation. In this study, an attempt has been made to fuse the characteristics of axial and sagittal view MR images using Canonical Correlation Analysis (CCA) for the differentiation of Healthy Controls (HC) and AD. For this, MR brain images obtained from a public database are skull stripped and spatially registered. Morphometric features are extracted from the pre-processed mid-sagittal and mid-axial images using histogram of oriented gradients. Further, these extracted features are fused using CCA. The performance of classifier is analyzed for the variations in canonical component dimensions. Results indicate that the morphometric feature spaces extracted from sagittal and axial planes individually overlap for HC and AD. The proposed CCA based fusion of sagittal and axial features exhibit variations between HC and AD images for a canonical feature dimension of 30. Performance of the adopted approach confirms that the bi-planar feature fusion is essential for the differentiation of AD.