Detection of Alzheimer's disease using advanced local binary pattern from hippocampus and whole brain of MR images

Alzheimer's disease as one type of dementia can cause problems to human memory, thinking and behavior. The brain damage can be detected using brain volume and whole brain form. The correlation between brain shrinkage and reduction of brain volume can affect to deformation texture. In this research, the enhancement texture approach was proposed, called advanced local binary pattern (ALBP) method. ALBP is introduced as a 2D and 3D feature extraction descriptor. In the ALBP, sign and magnitude value were introduced as an enhancement to the previous LBP method. Due to a great number of features are produced by ALBP, the principal component analysis (PCA) and factor analysis are used as feature selection method. Furthermore, SVM classifier is applied for multiclass classification including Alzheimer's, mild cognitive impairment, and normal condition of whole brain and hippocampus. The experimental results from two scenarios (ALBP sign magnitude (2D) and ALBP sign magnitude using three orthogonal planes (3D) methods) show better accuracy and performance compare to previous method. Our proposed method achieved the average value of accuracy between 80% - 100% for both the whole brain and hippocampus data. In addition, uniform rotation invariant ALBP sign magnitude using three orthogonal planes as a 3D descriptor also outperforms other approaches with an average accuracy of 96.28% for multiclass classifications for whole brain image.