Multi-resolution 3D-HOG feature learning method for Alzheimer’s Disease diagnosis☆
Introduction
Alzheimers Disease (AD) is a progressive neurodegenerative disease, which reflects anatomical atrophy or functional neurodegeneration of cerebral cortex. In recent years, some machine learning methods have been used to extract useful features from magnetic resonance imaging (MRI) scanned anatomical data to identify AD from Healthy Controls (HC). For feature extraction methods, three-dimension (3D) image based methods can effectively preserve the spatial feature information of MRI data compared with two-dimension (2D) image based methods. These methods directly or indirectly extract 3D features from MRI data using traditional feature extraction methods or deep learning methods respectively.
Most traditional feature extraction methods directly extract anatomical features of cerebral cortex from MRI images. Among them, the feature-based machine learning methods use the clinical parameters as features, such as the volume of gray matter, the cortical thickness, the mean curvature and area of cortical which are extracted by FreeSurfer image analysis suite [1]. Some image-based feature extraction methods extract features from different transform domain, such as texture-based Gabor transform method [2], multi-resolution-based discrete wavelet transform (DWT) method [3]. And there are other image-based methods, which directly extract image-based feature to describe the atrophy or shape changes of region-of-interests(ROIs) of the brain, such as ROIs-based sparse feature learning method [4], [5], local binary pattern (LBP) method [6] and histogram of oriented gradient (HOG) method [7], [8]. As an image gradient based feature extraction method, HOG method extracts image gradients within a region to reflect its edge gradient changes [9], which is used for region detection in medical images [10], [11]. Considering that there is volume atrophy and shape changes of cortical in AD, HOG method is also used to detect the local texture shape changes or gradient changes of the image for early diagnosis of AD. For example, Devvi et.al extracted HOG feature from three orthogonal of planes to describe the dynamic texture changes of MRI brain images [7]. Zhu et.al proposed a new multi-view learning method to learn the mappings from the HOG feature space to the ROI feature space, which uses 3D-HOG features as local features to reflect small or subtle changes within brain [12]. And in [8], small scale HOG features are extracted from ROIs and used to quantify spatial gradients of 18F-FDG PET images for AD diagnosis. 3D-HOG features represent the local texture changes within a volume statistic of spatial gradient and overcome the information loss generated from 2D-HOG representation. However, there are two main limits for 3D-HOG method: (1) features with same scale only represent local visual features with the same resolution, which cannot represent the characteristics of the image comprehensively. (2) HOG features consist of numerous histograms and thus cover invalid information. Effective characteristic should be obtained in additional step for accurate classification.
Although there are exhaustive extracted features, irrelevant or redundant features may reduce the efficiency of learning algorithms, i.e. not all extracted features are useful for the classification problems. As discussed in [13] and [14], feature extraction usually encounter the so-called ’High Dimension, Low Sample Size (HDLSS)’ problem. In order to resolve this problem, subspace learning methods and feature selection methods are used to reduce the feature dimensions to choose discriminating features. Subspace learning methods include linear methods, such as principle component analysis (PCA) [4], [15], [16], linear discriminant analysis (LDA) [17], [18], and non-linear methods, such as multi-kernel methods [19], [20]. Feature selection methods generally choose discriminative feature subset for the following classification, which are divided into class-dependent methods and class-independent methods. Class-independent feature selection methods choose potential features while ignoring different classes. Class-dependent feature selection methods utilize different feature subsets to discriminate different classes and obtain better performance than class-independent feature selection [21], which can be further divided into filter approaches and wrapper approaches depending on whether classifier is used or not. Filter approaches utilize various feature importance ranking methods for feature selection and selected features are used for comprehensive classification. For example, Minimal-RedundancyMaximal-Relevancy Measure (mRMR) method [22] selects attributes with maximal relevance and the minimal redundancy based on calculating the mutual information. RELIEF method [23] is a weighted method which tends to minimize intra-class distance and maximize inter-class distance. Class Separability Measure (CSM) method [24] is proceeded by calculating the intra-class and inter-class ratio which is used to evaluate the contribution of each attribute. The wrapper based method [21] finds discriminative feature subsets for each class and then uses the class-dependent subset for final classification. In [25], we proposed a wrapper-based feature selection method to rank the feature importance, which can select most important feature parameters or featured ROIs for AD identification compared with other feature selection methods.
As the image-based classification methods, deep learning methods use the whole brain or ROIs as input of the network to extract 2D or 3D features from MRI data indirectly. There are different network structures introduced to extract discriminate features from original images, such as convolution neural network (CNN), Residual Network (ResNet) [26], a combination of encoder-decoder network [27], U-Net [28], DenseNet [29], and transfer learning strategy [30]. Although these methods have shown good performance on AD classification, it is difficult to present interpretability on the extracted features or classification results because they incorporate feature extraction and classifier learning into an unified framework [31], [32]. Furthermore, the training outcome is unsatisfactory as the lack of samples [33].
In this paper, we proposed a ROIs-based multi-resolution 3D-HOG feature learning method for AD identification. Some preliminary accounts of this study were presented in our early conference papers [25], [34]. The main contributions of this paper can be concluded as follows:
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First, we proposed a multi-resolution 3D-HOG feature extraction method to describe local and global texture changes for AD identification, which can represent the characteristics of the image comprehensively compared with previous HOG-based approaches.
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Second, we proposed a histogram based wrapped feature selection method, which can not only select discriminative histograms with promising performance, but also detect distinct subareas of ROIs for AD identification.
The remaining of this paper is organized as follows: Section 2 presents the proposed feature extraction and feature selection method, Section 3 presents the experimental results, followed by the conclusion of this paper in Section 4.
Section snippets
Methodology
Framework of the proposed method is shown in Fig. 1. First, data preprocessing with MRI T1-weighted input is used to generate ROI-based images for feature extraction and classification. Second, 3D-HOG from various scales is extracted as basic feature unit. Third, spatial pyramid HOG features are constructed with multi-scale 3D-HOG features for informative representation. Finally, feature selection techniques are introduced to search for discriminative features and further efficient
Experimental result and discussion
In the following experiments, 20 times Monte-Carlo simulations are carried out to: (a) illustrate the effectiveness of SPHOG; (b) illustrate the effectiveness of the proposed feature selection method; (c) compare with other traditional machine learning methods and deep learning methods; (d) illustrate the effectiveness of the selected features. (e) illustrate the effectiveness of our proposed methods on multi-region based identification.
65 AD subjects and 65 HC subjects (totally 130 samples)
Conclusions
In this paper, we proposed a novel feature learning method for AD identification. From spatial pyramid representation, the multi-resolution SPHOG features are constructed to distinguish the deformation characteristics of cerebral cortex comprehensively. With the proposed histogram based wrappered feature selection algorithm, the discriminative SPHOG features are selected and the feature dimensions are reduced. Experimental results show that the selected SPHOG features outperforms other 3D-HOG
Declaration of Competing Interest
The authors declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
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Cited by (2)
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This work was supported by “National Natural Science Foundation of China (NSFC)” ( No. 61971106, 61603077, 41776204), “Fundamental Research Funds for the Central Universities”