A novel method and software for automatically classifying Alzheimer’s disease patients by magnetic resonance imaging analysis
Introduction
The Alzheimer’s Disease (AD) is one of the most common form of dementia, prevailing in people with over 65 years. In AD, normal brain tissues are degenerated and this fact can cause a reduction of memory and mental abilities. Magnetic resonance imaging does have a good ability for differentiating between soft tissues, thus providing useful information about organs like the brain. It is a tool that allows to diagnose many brain diseases including the Alzheimer’s disease. Multiple approaches successfully used MRI brain scans for abnormality/ normality Alzheimer assessment in the past [1], [2], [3], [4], [5]. Many other imaging modalities exists in order to study AD and other brain diseases, i.e., computed tomography (CT), positron emission tomography (PET) studies of cerebral metabolism with fluoro-deoxy-d-glucose (FDG), amyloid tracers such as Pittsburgh Compound-B (PiB), and [123I] FP-CIT SPECT [6], [7]. Among the plethora of methods being effectively applied in the classification of the Alzheimer’s disease, we briefly discuss those that are mostly related with the approach of this paper and with MRI.
Anandh et al. [8] propose to differentiate Mild Cognitive Impairment (MCI), Condition Normal (CN) and AD subjects from MRI scans using an approach based on Laplace-Beltrami eigenvalue shape descriptors. Laplace-Beltrami eigenvalues are infinite series of spectra that describes the intrinsic geometry of objects. Most significant Laplace–Beltrami shape descriptors are identified and their performance is analysed using linear Support Vector Machine (SVM) classifier.
Ramaniharan et al. [9] design another possible application of the Laplace–Beltrami eigenvalue shape descriptor for classifying the Alzheimer’s disease. The authors attempt to analyse the shape changes of corpus callosum using shape based Laplace-Beltrami eigenvalue features and machine learning techniques. Corpus callosum from the normal and AD T1-weighted MRI scans are segmented using the reaction diffusion level set approach and the results are validated against the ground-truth images. These values capture the shape information of corpus callosum by solving the eigenvalue problem of Laplace–Beltrami operator on the triangular meshes. The significant features are selected based on information gain ranking and subjected to classification using K-Nearest Neighbour and SVM. Furthermore, Guerrero et al. [10] propose a framework for feature extraction from low-dimensional subspaces that represent inter-subject variability. The manifold subspace is built from data-driven regions of interest. The regions are learnt via sparse regression using the mini-mental state examination score as an independent variable which correlates better with the actual disease stage than a discrete class label. The sparse regression is used to perform variable selection along with a re-sampling scheme to reduce sampling bias.
Conversely, Beheshti and Demirel [11] describe the use of t-test based feature-ranking approach as part of their feature extraction procedure, where the number of top features is determined using the Fisher criterion. The key aspect of this work is the use of a data fusion method among atrophy clusters to improve the classification performance. Additionally, Plocharski and Østergaard [12] design a technique to extract sulcal features by means of computing a sulcal medial surface for AD/CN classification. Zhou et al. [13] propose to combine MRI data with a neuropsychological test, mini-mental state examination as input to a multi-dimensional space for the classification of Alzheimer’s disease and its prodromal stages. The decisional space is constructed using those features deemed statistically significant via an elaborate feature selection [14] and ranking mechanism. Lastly, Daliri [15] proposes an approach based on feature extraction from MRI scans. The author presents an automated method for diagnosing Alzheimer’s disease from brain MRI scans, where a computer vision feature extractor, called Scale Invariant Feature Transforms (SIFT) [16], is used to extract important features for tackling the classification task. Finally, the reader may refer to following review papers that deal with the topic of feature extraction [17], [18], [19], [20], [21] and registration [7], [22], [23], [24] in imaging.
Contributions. The purpose of this work is to present an automated approach for classifying Alzheimer’s disease patients from magnetic resonance imaging brain scans. We propose to use a set of features derived from the concept of key points [16], representing relevant information of the images. The key points are extracted with a recent feature extraction technique, called Oriented FAST and Rotated BRIEF (ORB) [25]. One of the main contribution of this work is how the final set of features is obtained, defining and combining two new metrics, i.e., the spatial position of the extracted key points and their distribution around the patient’s brain. Our proposed method is fast and reliable for a straightforward deploy in clinical applications.
Availability. The complete software package and the documentation is open-source and publicly available at https://github.com/fabioprev/MRI-Classifier.git.
Section snippets
Methodology
In this work, we propose an approach for classifying magnetic resonance imaging scans based on a suitable choice of the features. Features may be specific structures in the image such as points, edges or objects, but they may also be the result of a general neighbourhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, to shapes defined in terms of curves or boundaries between different image regions, or to properties of
Experimental evaluation
We now report the quantitative evaluation of the proposed approach described in Section 2.
Conclusions
In this work, we presented an automatic approach for classifying the Alzheimer’s disease from MRI patient brain scans. We demonstrated that the method is fast and reliable for a suitable and straightforward deploy in clinical applications for improving the efficacy of medical treatments by recognising the disease’s state of the patient. The method uses the Oriented FAST and Rotated BRIEF technique for extracting the salient features from each MRI patient brain scan. One of the main contribution
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2021, Computer Methods and Programs in BiomedicineCitation Excerpt :These inherent features combined with clinical medical data and genetic data were used to build a classifier to predict the changing trends of subjects at different stages [13]. F. Previtali et al. proposed a feature extraction technique from patients’ MRI brain scans [14]. Zhang et al. proposed a new multi-view clustering model called Consensus Multi-view Clustering (CMC) based on nonnegative matrix factorization for predicting the multiple stages of AD progression [15].
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2021, Physica MedicaCitation Excerpt :Structural MRI, based on high resolution T1 weighted imaging (3D-T1 MRI) has excellent contrast among soft tissues in brain, but diffusion tensor imaging (DTI) [3,114] or resting-state functional MRI (rsfMRI), are increasingly used for characterizing the brain activity. Accurate classification of Alzheimer disease (AD) and mild cognitive impairment (MCI) subjects has been achieved by combining novel topological descriptors and ML algorithms [115–121]. Volume descriptors or radiomic features [65] from MRI, alone or in combination with results from visuospatial tests were used in SVM classifiers to distinguish mild AD patients from healthy controls [76].
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2021, Computer Methods and Programs in BiomedicineCitation Excerpt :Liu et al. provided a multi-scale modeling variantto-function-to-network model to inquire into the causal effect of rare noncoding variants for AD [28]. Previtali et al. proposed a novel feature extraction method from brain MRI scans for classifying AD patient [41]. Vaithinathan et al. presented a new algorithm for the classification problems of AD, which belongs to a kind of texture extraction technique with T1 weighted MRI [49].