Title:Discriminative Sparse Features for Alzheimer's Disease Diagnosis Using Multimodal Image Data
Volume: 15
Issue: 1
Author(s): Andres Ortiz, F. Lozano, Juan M. Górriz*, Javier Ramírez, Francisco J. Martínez Murcia and for the Alzheimer’s Disease Neuroimaging Initiative
Affiliation:
- Department of Signal Theory, Networking and Communications, 18071 University of Granada, Granada,Spain
Keywords:
Sparse features, multimodel data, mild cognitive impairment, support vector classifiers, computer aided diagnosis,
ADNI.
Abstract: Background: Feature extraction in medical image processing still remains a challenge, especially
in high-dimensionality datasets, where the expected number of available samples is considerably
lower than the dimension of the feature space. This is a common problem in real-world data, and, specifically,
in medical image pro- cessing as, while images are composed of hundreds of thousands voxels,
only a reduced number of patients are available.
Objective: Extracting descriptive and discriminative features to represent each sample (image) by a small
number of features, which is particularly important in classification task, due to the curse of dimensionality
problem.
Methods: In this paper we solve this recognition problem by means of sparse representations of the data,
which also provides an arena to multimodal image (PET and MRI) data classification by combining specialized
classifiers. Thus, a novel method to effectively combine SVC classifiers is presented here, which
uses the distance to the hyperplane computed for each class in each classifier allowing to select the most
discriminative image modality in each case. The discriminative power of each modality also provides
information about the illness evolution; while functional changes are clearly found in Alzheimer’s diagnosed
patients (AD) when compared to control subjects (CN), structural changes seem to be more relevant
at the early stages of the illness, affecting Mild Cognitive Impairment (MCI) patients.
Results: Classification experiments using 68 CN, 70 AD and 111 MCI images from the Alzheimer's Disease
Neuroimaging Initiative database have been performed and assessed by cross-validation to show the
effectiveness of the proposed method. Accuracy values of up to 92% and 84% for CN/AD and CN/MCI
classification are achieved.
Conclusions: The method presented in this work shows that sparse representations of brain images are of
importance for codifying and transferring relevant image features, as they may capture the salient features
while maintaining lightweight data transactions. In fact, the method proposed in this work outperforms
the classification results obtained using projection methods such as Principal Component Analysis for
extracting representative features of the images.
