Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Images

Abstract

In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) High-level statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.

A classification for complex imbalanced data in disease screening and early diagnosis

“…In the literature, biomarkers from different modalities have been utilized to investigate the progression of AD. Brain abnormalities detected by MRI are considered to be valid markers of AD and are widely used to predict the conversion from MCI to AD 56‐60 . Fluorodeoxyglucose positron emission tomography (FDG‐PET) is able to provide the estimates of cerebral metabolic rates of glucose, thus revealing the pattern of regional hypometabolism which is a prominent hallmark of AD 61‐64 .…”

Anatomical Landmarks and DAG Network Learning for Alzheimer’s Disease Diagnosis

“…In the patch-based methods, a scale between voxel and region level is used to extract features with more information, which not only considers the subtle changes of brain tissue, but also reduces the feature dimension and avoids overfitting. For example, the discriminating anatomical landmarks are identified as image features by utilizing the morphological characteristics of voxels [33]- [36]. A deep learning framework ensemble based on landmarks is proposed for disease diagnosis [37]- [39].…”

Computer-Aided Multi-Target Management of Emergent Alzheimer’s Disease

“…Early diagnosis plays an important role in preventing progress [ 41 , 47 , 53 ] or lateonset of AD. Treatment for AD is based on features in brain image [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 61 , 62 , 63 , 64 , 65 ]. The features include AD-related variations of anatomical brain structures such as ventricles size, hippocampus shape, cortical thickness, and brain volume.…”