Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis

• Published in: IEEE transactions on medical imaging Vol. 35; no. 12; pp. 2524 - 2533
• Main Authors: Zhang, Jun, Gao, Yue, Gao, Yaozong, Munsell, Brent C., Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Alzheimer’s disease (AD); Anatomic Landmarks - diagnostic imaging; Approximation; Brain; Brain - diagnostic imaging; Classification; Diagnosis; Error detection; Feature extraction; Humans; Image classification; Image Interpretation, Computer-Assisted - methods; Image processing; Image registration; Image segmentation; landmark detection; Magnetic resonance imaging; magnetic resonance imaging (MRI); Magnetic Resonance Imaging - methods; Medical diagnosis; Morphology; Neurodegenerative diseases; Neuroimaging; NMR; Nuclear magnetic resonance; Regression Analysis; regression forest; Shape; Support vector machines; Testing; Training
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2016.2582386

Structural magnetic resonance imaging (MRI) is a very popular and effective technique used to diagnose Alzheimer's disease (AD). The success of computer-aided diagnosis methods using structural MRI data is largely dependent on the two time-consuming steps: 1) nonlinear registration across subjects, and 2) brain tissue segmentation. To overcome this limitation, we propose a landmark-based feature extraction method that does not require nonlinear registration and tissue segmentation. In the training stage, in order to distinguish AD subjects from healthy controls (HCs), group comparisons, based on local morphological features, are first performed to identify brain regions that have significant group differences. In general, the centers of the identified regions become landmark locations (or AD landmarks for short) capable of differentiating AD subjects from HCs. In the testing stage, using the learned AD landmarks, the corresponding landmarks are detected in a testing image using an efficient technique based on a shape-constrained regression-forest algorithm. To improve detection accuracy, an additional set of salient and consistent landmarks are also identified to guide the AD landmark detection. Based on the identified AD landmarks, morphological features are extracted to train a support vector machine (SVM) classifier that is capable of predicting the AD condition. In the experiments, our method is evaluated on landmark detection and AD classification sequentially. Specifically, the landmark detection error (manually annotated versus automatically detected) of the proposed landmark detector is 2.41 mm, and our landmark-based AD classification accuracy is 83.7%. Lastly, the AD classification performance of our method is comparable to, or even better than, that achieved by existing region-based and voxel-based methods, while the proposed method is approximately 50 times faster.