Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

A novel multimodal medical image fusion using sparse representation and modified spatial frequency

Fusion of multimodal imaging data supports medical experts with ample information for better disease diagnosis and further clinical investigations. Recently, sparse representation (SR)‐based fusion algorithms has been gaining importance for their high performance. Building a compact, discriminative dictionary with reduced computational effort is a major challenge to these algorithms. Addressing this key issue, we propose an adaptive dictionary learning approach for fusion of multimodal medical images. The proposed approach consists of three steps. First, zero informative patches of source images are discarded by variance computation. Second, the structural information of remaining image patches is evaluated using modified spatial frequency (MSF). Finally, a selection rule is employed to separate the useful informative patches of source images for dictionary learning. At the fusion step, batch‐OMP algorithm is utilized to estimate the sparse coefficients. A novel fusion rule which measures the activity level in both spatial domain and transform domain is adopted to reconstruct the fused image with the sparse vectors and trained dictionary. Experimental results of various medical image pairs and clinical data sets reveal that the proposed fusion algorithm gives better visual quality and competes with existing methodologies both visually and quantitatively.     

Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing subjects into AD patients and cognitively normal (CN) individuals were built using grey matter (GM) and white matter (WM) volumetric features extracted from structural magnetic resonance (MR) images. It could be observed from results that the classifier built using both GM and WM features provided accuracy of 89.26% which is better than the performance of classifiers built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features improves the accuracy further to 94.63% which is better than the performance reported by recent works in literature. In order to identify the brain regions that are important for AD vs CN classification problem, we used SRPSO to extract GM and WM features that yield better classification performance. Using 50 features identified by SRPSO, an accuracy of 89.39% was obtained which is close to the accuracy based on all features. The features identified by SRPSO were mapped back to the brain to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD.     

3d discrete wavelet transform for computer aided diagnosis of Alzheimer's disease using t1‐weighted brain MRI

Early and antemortem diagnosis of Alzheimer's disease (AD) may help in the development of appropriate treatment and in slowing down the disease progression. In this work, a three‐phase computer aided approach is suggested for classification of AD patients and controls using T1‐weighted MRI. In the first phase, smoothed modulated gray matter (GM) probability maps are obtained from T1‐weighted MRIs. In the second phase, 3D discrete wavelet transform is applied on GM of five brain regions, which are well‐documented regions affected in AD, to construct features. In the third phase, a minimal set of relevant and nonredundant features are obtained using Fisher's discriminant ratio and minimum redundancy maximum relevance feature selection methods. To check the efficacy of the proposed approach, experiments were carried out on three datasets derived from the publicly available OASIS database, using three commonly used classifiers. The performance of the proposed approach was evaluated using three performance measures namely sensitivity, specificity and classification accuracy. Further, the proposed approach was compared with the existing state‐of‐the‐art techniques in terms of three performance measures, ROC curves, scoring and computation time. Irrespective of the datasets and the classifiers, the proposed method outperformed the existing methods. In addition, the statistical test also demonstrated that the proposed method is significantly better in comparison to the other existing methods. The appreciable performance of the proposed method supports that it will assist clinicians/researchers in the classification of AD patients and controls.     

Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication

Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In multimodal biometric recognition, score level fusion has been a very promising approach to improve the overall system's accuracy. In this paper, score level fusion is carried out using three categories of classifiers like, rule classifier (fuzzy classifier), lazy classifier (Naïve Bayes) and learning classifiers (ABC-NN). These three classifiers have their own advantages and disadvantages so the hybridization of classifiers leads to provide overall improvements. The proposed technique consists of three modules, namely processing module, classifier module and combination module. Finally, the proposed fusion method is applied to remote biometric authentication. The implementation is carried out using MATLAB and the evaluation metrics employed are False Acceptance Rate (FAR), False Rejection Rate (FRR) and accuracy. The proposed technique is also compared with other techniques and by employing various combinations of modalities. From the results, we can observe that the proposed technique has achieved better accuracy value and Receiver Operating Characteristic (ROC) curves when compared to other techniques. The proposed technique reached maximum accuracy of having 95% and shows the effectiveness of the proposed technique.     

On the application of the stochastic approach in predicting fatigue reliability using Miner's damage rule

The present paper investigates the application of the stochastic approach when the commonly adopted Miner's linear damage rule is implemented, both in its traditional and modified forms to include the presence of a random stress threshold (random fatigue limit), below which the rate of damage accumulation is reduced. Main steps are provided to obtain the simulated distribution of the accumulated damage under variable amplitude loading. When the stochastic approach is applied in the presence of a random fatigue limit, an additional correlation structure, which takes into account the fatigue limit value, must be introduced in the analysis. If the number of cycles to failure under constant amplitude loading is Weibull (Log‐Normal) distributed, then the corresponding accumulated damage is Fréchet (Log‐Normal) distributed. The effects of the correlation structure on reliability prediction under variable amplitude loading are also investigated. To this aim, several experimental datasets are taken from the literature, covering various metallic materials and variable amplitude block sequences. The results show that the choice of the damage accumulation model is a key factor to value the improvement in the accuracy of reliability predictions introduced by the stochastic approach. Comparison of the predicted number of cycles to failure with experimental data shows that larger errors are non‐conservative, regardless of the adopted correlation structure. When the analysis is limited to reliability levels above 80%, for these large non‐conservative errors, it is the quantile approach to be closer to actual experimental data, thus limiting the overestimation of component's life. For the experimental datasets considered in the paper, adoption of a stochastic approach would improve the accuracy of Miner's predictions in 10% of cases.     

Light‐Induced ROS Generation and 2‐DG‐Activated Endoplasmic Reticulum Stress by Antitumor Nanosystems: An Effective Combination Therapy by Regulating the Tumor Microenvironment

A multimodal cancer therapeutic nanoplatform is reported. It demonstrates a promising approach to synergistically regulating the tumor microenvironment. The combination of intracellular reactive oxygen species (ROS) generated by irradiation of photosensitizer and endoplasmic reticulum (ER) stress induced by 2‐deoxy‐glucose (2‐DG) has a profound effect on necrotic or apoptotic cell death. Especially, targeting metabolic pathway by 2‐DG is a promising strategy to promote the effect of photodynamic therapy and chemotherapy. The nanoplatform can readily release its cargoes inside cancer cells and combines the advantages of ROS‐sensitive releasing chemotherapeutic drugs, upregulating apoptosis pathways under ER stress, light‐induced generation of cytotoxic ROS, achieving tumor accumulation, and in vivo fluorescence imaging capability. This work highlights the importance of considering multiple intracellular stresses as design parameters for nanoscale functional materials in cell biology, immune response, as well as medical treatments of cancer, Alzheimer's disease, etc.     

Topology optimization with incompressible materials under small and finite deformations using mixed u/p elements

This paper focuses on topology optimization utilizing incompressible materials under both small‐ and finite‐deformation kinematics. To avoid the volumetric locking that accompanies incompressibility, linear and nonlinear mixed displacement/pressure (u/p) elements are utilized. A number of material interpolation schemes are compared, and a new scheme interpolating both Young's modulus and Poisson's ratio (E ‐ν interpolation) is proposed. The efficacy of this proposed scheme is demonstrated on a number of examples under both small‐ and finite‐deformation kinematics. Excessive mesh distortions that may occur under finite deformations are dealt with by extending a linear energy interpolation approach to the nonlinear u/p formulation and utilizing an adaptive update strategy. The proposed optimization framework is demonstrated to be effective through a number of representative examples.     

孔洞特征模型特征分割及自适应分层算法

目的 针对传统整体式3D打印分层算法对孔洞特征模型分层处理时层厚不够合理、难以较好平衡成形精度和成形效率等问题,通过改进分层算法,实现了孔洞特征模型3D打印成形精度和成形效率的提升。方法 首先确定模型上下水平面区域,剔除该区域的三角面片,使孔洞特征三角面片与其他部分处于非邻接状态,其次通过建立三角面片拓扑结构,将模型孔洞特征包含的三角面分离,最后以当前层相交三角面片法向量与z轴最小夹角为层厚判据,分别对模型孔洞特征及其他部分进行自适应分层,并将模型各部分分层路径整合,形成完整的模型3D打印机执行代码。结果 示例模型分层模拟及实际打印结果表明,对于实验所用的螺母模型和拇指轮模型,与三角面片法向量自适应分层算法相比,所提算法的模型成形精度分别提升了20.18%和62.68%,打印耗时分别缩短了34 min和11 min。结论 对于孔洞特征模型,采用分离模型孔洞特征与单独自适应分层的策略,能够较好地提升模型成形精度,并且缩短模型成形时间。     

Alzheimer disease classification using KPCA,LDA, and multi‐kernel learning SVM

Early diagnosis of Alzheimer disease (AD) and mild cognitive impairment (MCI) is always useful. Preventive measures might have an impact on reducing AD risk factors. Structural magnetic resonance (MR) imaging, one of the vital sensitive biomarkers for cerebral atrophy in the brain, is used to extract volumetric feature by FreeSurfer and the CIVET toolbox. All of the structural magnetic resonance imaging (s‐MRI) data that we used were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) of imaging data. This novel approach is applied for the diagnosis of AD and MCI from healthy controls (HCs) combining extracted features with the MMSE (mini‐mental state examination) scores, applying a two sample t‐test to select a subset of features. The subset of features is fed to kernel principal component analysis (KPCA) module to project data onto the reduced principal component coefficients at higher dimensional kernel space to increase the linear separability. Then, the kernel PCA coefficients are projected into the more efficient linear discriminant space using linear discriminant analysis. A multi‐kernel learning support vector machine (SVM) is used on newly projected data for stratification of AD and MCI from HCs. Using this approach, we obtain 93.85% classification accuracy when detecting AD from HCs for segmented volumetric features (using FreeSurfer) with high sensitivity and specificity. When distinguishing MCI from HCs and AD using volumetric features after subcortical segmentation, the detection rate reaches 86.54% and 75.12%, respectively.     

Automated differentiation of skin melanocytes from keratinocytes in high-resolution histopathology images using a weakly-supervised deep-learning framework

Early detection and accurate diagnosis of melanoma skin cancer which accounts for 75% of all skin cancer mortalities would significantly improve survival rates. Melanoma is characterized by an abnormal proliferation of malignant melanocytes in the epidermis of the skin, which is composed predominantly of keratinocytes. Localization and differentiation of melanocytes from keratinocytes in the skin in whole slide images (WSIs) is an important initial task towards the diagnosis of the tumor. Because of the small size of melanocytes and the dominance of keratinocytes in the epidermis, identification of melanocytes can be challenging and prone to errors. We propose a new fully-automated framework based on deep-machine learning to identify melanocytes and keratinocytes with detection accuracy of 90.5% and 87.4%, respectively. This framework begins with segmenting the epidermis layer inside 640 × 320 × 3 super tiles of WSIs using a DeepLabV3+ model followed by a weakly-supervised deep learning approach deploying a fine-tuned pre-trained visual geometry group (VGG)-16 model, gradient-weighted class activation mapping (Grad-CAM), Otsu's and contour estimation methods in order to identify melanocytes and keratinocytes inside each 64 × 64 × 3 tile (i.e., a subdivision of the super tile). The proposed framework outperforms the state-of-the-art methods as well as provides a significant improvement of 28% and 17% over the fully supervised faster region-based convolutional neural network (R-CNN) in detecting melanocytes and keratinocytes respectively without the need for expensive expert fine labels for model training and validation. The proposed framework offers a promising accurate tool to aid pathologists in differentiating melanocytes from keratinocytes that would eventually support the diagnosis of melanoma.     

A novel region-based multimodal image fusion technique using improved dictionary learning

Recently, the sparse representation (SR) based algorithms have gained much attention from the researchers in the area of image fusion (IF). The building of a compact discriminative dictionary plays a vital role in the sparse-based IF techniques. In this context, an efficient multimodal IF method based on improved dictionary learning is investigated. The key contributions of this paper are: (a) An improved KSVD algorithm is suggested for the dictionary learning process, (b) to reduce the computational time, only the informative patches are selected using energy feature, and (c) a novel region-based fusion scheme is suggested for the first time for the problem on hand. The suggested technique is tested with a number of multimodal images from Harvard Medical School brain database. The results are compared with state-of-the-art multiscale transform-based methods and modified SR-based methods. Unlike earlier methods, our proposed technique generates an adaptive dictionary through selection of informative patches only. This results in a compact dictionary with improved computational efficiency. The experimental results reveal that our approach outperforms other methods. The potential application of the suggested method could be in pathological images for follow-up study and better treatment planning.     

基于C-B样条的三角形和四边形曲面生成

文章给出了基于C-B样条的由网格数据产生三角形和四边形曲面片的方法,C-B样条是由基底函数{sin t,cos t,t,1}导出的一种新型样条曲线,它可以克服现在正在使用的B样条和有理B样条为了满足数据网格的拓扑结构而增加多余的控制点,求导求积分复杂繁琐,阶数过高,从而讨论其连续拼接时增加了困难等缺点,如何将它推广成曲面就成为一个重要问题。作者利用边-顶点方法构造插值算子,再将这些算子进行凸性组合,将C-B样条曲线推广成三角形曲面片和四边形曲面片,它可以用于CAD的逆向工程中散乱数据的曲面重构。     

用于声音分类的Deep LightGBM算法

在医学诊断、场景分析、语音识别、生态环境分析等方面语音分类都有着广泛的应用价值。传统的语音分类器采用的是神经网络。但是在精确度,模型设置,参数调整和资料的预处理等方面,有较大的缺陷。在这一基础上,文章提出了一种以“深度森林”为基础的改进方法——LightGBM的深度学习模型（Deep LightGBM模型）。它能够在保证模型简洁的前提下,提高分类精度和泛化能力。该算法有效降低了参数依赖性。在UrbanSound8K这一数据集中,采用向量方法进行语音特征的提取,其分类精确度达95.84%。将卷积神经网络（Convolutional Neural Net‐work,CNN）抽取的特征和向量法获取的特征进行融合,并利用新的模型进行训练,其准确率可达97.67%。实验证明,此算法采用的特征提取方式与Deep LightGBM配合获得的模型参数调整容易,精度高,不会产生过度拟合,并且泛化能力好。     

基于成形工具姿态调整的适应性积层制造技术研究

当前的积层制造技术主要采用2.5D切片。成形中台阶效应不可避免，本文提出了一种新的基于3D切片的适应性成形方法。通过增加成形工具的运动自由度，使之能根据实体的几何外形自动调整姿态，从而有效地克服了台阶效应对成形精度的影响。在保持同等成形精度的前提下。采用适应性斜切成形技术可以使用更厚的层片。提高成形效率，实现了成形精度和速度的兼得。     

Comparative study of finite element formulations for the semiconductor drift-diffusion equations

A number of transient and steady-state finite element formulations of the semiconductor drift-diffusion equations are studied and compared with respect to their accuracy and efficiency on a simple test structure (the Mock diode). A new formulation, with a consistent interpolation function used to represent the electron and hole carrier densities throughout the set of semiconductor drift-diffusion and Poisson's equations, is introduced. Results highlight the advantages in using consistent interpolation functions showing an increased accuracy in the calculated values and a saving in data storage and execution time. The results also illustrate how the use of different time integration methods affect the number of time steps required during transient simulations. The combination of the fully consistent DFUS with appropriate time integration methods is found to yield a saving of up to 80 per cent of the execution time required for standard spatial/temporal discretization techniques. © 1997 by John Wiley & Sons, Ltd.     

Numerical simulations of large deformation of thin shell structures using meshfree methods

 In this paper, meshfree simulations of large deformation of thin shell structures is presented. It has been shown that the window function based meshfree interpolants can be used to construct highly smoothed (high order “manifold”) shape functions for three-dimensional (3-D) meshfree discretization/interpolation, which can be used to simulate large deformation of thin shell structures while avoiding ill-conditioning as well as stiffening in numerical computations. The main advantage of such 3-D meshfree continuum approach is its simplicity in both formulation and implementation as compared to shell theory approach, or degenerated continuum approach. Moreover, it is believed that the accuracy of the computation may increase because of using 3-D exact formulation. Possible mechanism to relieve shear/volumetric locking due to the meshfree interpolation is discussed. Several examples have been computed by using a meshfree, explicit, total Lagrangian formulation. Towards to developing a self-contact algorithm, a novel meshfree contact algorithm is proposed in the end.     

Providing multimodal biometric authentication using five competent traits

Abstract

Recognition accuracy of a single biometric authentication system is often much limited; hence, a multimodal biometric approach for identity verification is proposed. A new way of person authentication based on five-competent traits, namely, iris, ear, palm print, fingerprint and retina, is proposed in this paper. Each metric is analysed individually to get the matching scores from the corresponding feature sets. These scores are then combined using weighted sum fusion rule. In order to provide liveness verification for our authentication system, we employ the retinal blood vessel pattern recognition. To validate our approach, several experiments were conducted on the images obtained from five different datasets. The experimental results reveal that this multimodal biometric verification system is much more reliable and precise than the single biometric approaches.     

Neuroimaging computer-aided diagnosis systems for Alzheimer's disease

This paper has reviewed the state-of-the-art approaches for Computer Aided Diagnosis Systems (CADS) for Alzheimer's Disease (AD) using neuroimaging. Identification of the current approaches leads to improving the efficiency of these techniques. The analysis covered 110 articles published between 2009 and January 2018. Papers were chosen according to the Newcastle-Ottawa criteria. MeSH terms were “computer aided diagnosis systems for Alzheimer's disease” and “computer aided diagnosis systems methods for diagnosis of Alzheimer's disease”. CADS algorithms have been presented with specific methods. There is no standardized approach to determine the best one. This study has tables that aimed to conclude all methods in a precise way. Among them, Statistical Parametric Mapping (SPM), Principal Component Analysis (PCA), and Support Vector Machine (SVM) were the most common, respectively. CADS for AD could become important in clinical practice in the near future. The evaluation criteria approved their efficiency as a second opinion besides the neurologist.