Feature selection of manifold learning using principal component analysis in brain MR image

Cerebral atrophy treated as one of the common feature of many diseases that affect the brain. In general, atrophy means that all of the brain has shrunk or it can be regional, affecting a limited area of the brain which ends up resulting in neural decrease related to functions that area of brain controls. Detection of early brain atrophy can help physicians to detect the disease at curable stage. In this paper brain atrophy with some given landmark positions has been evaluated using dimensionality reduction methods. A comparative study has been done between principal component analysis and manifold learning using Laplacian eigenmaps to quantify brain atrophy. In addition, a novel method has been proposed with combination of PCA and Manifold learning which evaluates brain atrophy with corresponding age groups. Selection of principal component scores to optimize manifold learning parameters added effective feature to the findings. The method has been applied to open database (IXI database). We applied principal component analysis to deformation maps derived from MR images of 250 normal subjects. After sampling, 42 subjects were taken whose principal component scores were used to discriminate between older subject and younger subject. We found a significant regional pattern of atrophy between distance of Anterior Commissure, Posterior Commissure, Anterior Commissure to both frontal lobe, Posterior Commissure to both frontal lobe with corresponding age. After going through T-test principal component analysis showed the best value of significant difference (p<0.0036) over the Manifold learning (p<0.4095). The proposed method outperformed both the dimensionality reduction method with a score of (p<0.0030). Our findings indicates that multivariate network analysis of deformation maps detects typical feature of atrophy and provides a powerful tool to predict brain atrophy with age.