Classification of 3D texture features based on MR image in discrimination of Alzheimer disease and mild cognitive impairment from normal controls

Objective To discriminate Alzheimer disease(AD) and mild cognitive impairment(MCI) from normal controls with 3D texture features,in order to explore the new approach for the early diagnosis of AD.Methods 3D texture analysis was performed on MR images of 12 early AD patients(AD group),12 MCI patients(MCI group) and 12 normal controls(NC group).Texture features of the hippocampus and corpus callosum were extracted from gray level co-occurrence matrix and run length matrix.The texture features that existed significant differences among groups were used as features in a classification procedure based on support vector machines(SVM).The accuracy was evaluated with leave-one-out cross-validation.Results The classification accuracy for NC and MCI group,MCI and AD group,NC and AD group was 79.17%,83.33% and 91.67%,respectively.Conclusion 3D texture characteristics can be used to discriminate patients with early AD and patients with MCI from normal controls,and would be helpful to early diagnosis of AD.