High-dimensional Feature Extraction Research Articles

Radiomics of pituitary adenoma using computer vision: a review.

Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of "Radiomics" involves the extraction of high-dimensional features, often referred to as "Radiomic features," from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.

Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high-dimensional features in imaging can assist doctors in clinical decision-making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct.

Reservoir Prediction Model via the Fusion of Optimized Long Short-Term Memory Network (LSTM) and Bidirectional Random Vector Functional Link (RVFL)

An accurate and stable reservoir prediction model is essential for oil location and production. We propose an predictive hybrid model ILSTM-BRVFL based on an improved long short-term memory network (IAOS-LSTM) and a bidirectional random vector functional link (Bidirectional-RVFL) for this problem. Firstly, the Atomic Orbit Search algorithm (AOS) is used to perform collective optimization of the parameters to improve the stability and accuracy of the LSTM model for high-dimensional feature extraction. At the same time, there is still room to improve the optimization capability of the AOS. Therefore, an improvement scheme to further enhance the optimization capability is proposed. Then, the LSTM-extracted high-dimensional features are fed into the random vector functional link (RVFL) to improve the prediction of high-dimensional features by the RVFL, which is modified as the bidirectional RVFL. The proposed ILSTM-BRVFL (IAOS) model achieves an average prediction accuracy of 95.28%, compared to the experimental results. The model’s accuracy, recall values, and F1 values also showed good performance, and the prediction ability achieved the expected results. The comparative analysis and the degree of improvement in the model results show that the high-dimensional extraction of the input data by LSTM is the most significant improvement in prediction accuracy. Secondly, it introduces a double-ended mechanism for IAOS to LSTM and RVFL for parameter search.

A High-Capacity Steganography Algorithm Based on Adaptive Frequency Channel Attention Networks.

Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.

Severity Estimation for Interturn Short-Circuit and Demagnetization Faults through Self-Attention Network.

This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based on a self-attention module. The self-attention mechanism enhances the high-dimensional feature extraction and regression ability of the network by concentrating on specific sequence representations, thereby supporting the estimation of the fault severities. The proposed strategy can diagnose a hybrid fault in which the ISCF and DF occur simultaneously and does not require the exact model and parameters essential for the existing method for estimating the fault severity. The effectiveness and feasibility of the proposed fault diagnosis strategy are demonstrated through experimental results based on various fault cases and load torque conditions.

Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.

Optimization Method of Power Equipment Maintenance Plan Decision-Making Based on Deep Reinforcement Learning

The safe and reliable operation of power grid equipment is the basis for ensuring the safe operation of the power system. At present, the traditional periodical maintenance has exposed the abuses such as deficient maintenance and excess maintenance. Based on a multiagent deep reinforcement learning decision-making optimization algorithm, a method for decision-making and optimization of power grid equipment maintenance plans is proposed. In this paper, an optimization model of power grid equipment maintenance plan that takes into account the reliability and economics of power grid operation is constructed with maintenance constraints and power grid safety constraints as its constraints. The deep distributed recurrent Q-networks multiagent deep reinforcement learning is adopted to solve the optimization model. The deep distributed recurrent Q-networks multiagent deep reinforcement learning uses the high-dimensional feature extraction capabilities of deep learning and decision-making capabilities of reinforcement learning to solve the multiobjective decision-making problem of power grid maintenance planning. Through case analysis, the comparative results show that the proposed algorithm has better optimization and decision-making ability, as well as lower maintenance cost. Accordingly, the algorithm can realize the optimal decision of power grid equipment maintenance plan. The expected value of power shortage and maintenance cost obtained by the proposed method is $71.75$ $MW·H$ and $496000$ $yuan$.

IMG-10. MRI-BASED RADIOMIC PROGNOSTIC MARKERS OF DIFFUSE MIDLINE GLIOMA

BACKGROUNDDiffuse midline gliomas (DMG) are lethal pediatric brain tumors with dismal prognoses. Presently, MRI is the mainstay of disease diagnosis and surveillance. We aimed to identify prognostic image-based radiomics markers of DMG and compare its performance to clinical variables at presentation.METHODS104 treatment-naïve DMG MRIs from five centers were used (median age=6.5yrs; 18 males, median OS=11mos). We isolated tumor volumes of T1-post-contrast (T1gad) and T2-weighted (T2) MRI for PyRadiomics high-dimensional feature extraction. 900 features were extracted on each image, including first order statistics, 2D/3D Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Neighboring Gray tone Difference Matrix, and Gray Level Dependence Matrix, as defined by Imaging Biomarker Standardization Initiative. Overall survival (OS) served as outcome. 10-fold cross-validation of LASSO Cox regression was used to predict OS. We analyzed model performance using clinical variable (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variable. Concordance metric was used to assess the Cox model.RESULTSNine radiomic features were selected from T1gad (2 texture wavelet) and T2 (5 first-order features (1 original, 4 wavelet), 2 texture features (1 wavelet, 1 log-sigma). This model demonstrated significantly higher performance than a clinical model alone (C: 0.68 vs 0.59, p<0.001). Adding clinical features to radiomic features slightly improved prediction, but was not significant (C=0.70, p=0.06).CONCLUSIONOur pilot study shows a potential role for MRI-based radiomics and machine learning for DMG risk stratification and as image-based biomarkers for clinical therapy trials.

Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction

High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method—minimum eigenvector collaborative representation discriminant projection—to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size.

How to Extract Image Features Based on Co-Occurrence Matrix Securely and Efficiently in Cloud Computing

High-dimensional feature extraction based on co-occurrence matrix improves the detection performance of steganalysis, but it is difficult to be realized for massive image data by an analyzer with limited computational ability. We solve this problem by verifiable outsourcing computation, which allows a computationally weak client to outsource the evaluation of a function to a powerful but untrusted server. In this paper, we propose a verifiable outsourcing scheme of feature extraction based on co-occurrence matrix with single untrusted cloud server. The original images are protected from the server by using a projection of one to many with trapdoor, which can be realized by a symmetric probabilistic encryption scheme we present. The analyzer can obtain true results of feature extraction and detect any failure with a probability of 1 if the server misbehaves. Finally, we provide the simulations on the outsourcing of extracting ccJRM features in cloud computing. The theory analysis and experiment result also show that the proposed outsourcing scheme could greatly decrease the computation cost of the analyzer without exposure of the original images and extraction results.

Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning

The utilization of wind power is influenced by the fluctuation of the wind, to further strengthen the prediction accuracy of wind speed, two novel hybrid models uniting signal processing, deep learning and ensemble learning were proposed. Firstly, the wind speed series was disaggregated by the wavelet transform (WT). Then to enhance the forecasting precision of the subseries, the deep belief network (DBN) was applied to extract the high dimensional features. Besides, to overcome the limitation of the conventional DBN, the forecasts for each subseries processed by DBN were executed by the light gradient boosting machine (LGBM) and the random forest (RF). Some experiments have been accomplished, where the promotion of high dimensional feature extraction through DBN was explored. Meanwhile, the development of forecasting accuracy by applying tree-based models was confirmed, and the differences between these two hybrid models were discussed. It is shown that: (1) In comparison with the persistence method, the Elman neural network (ENN), DBN, LGBM, and RF, the hybrid models show a great boost in prediction accuracy. (2) The high dimensional feature extraction through DBN is in favor of improving the predicting accuracy of tree-based models, and tree-based models would facilitate the prediction. (3) Between the proposed models, the hybrid model integrated with RF slightly outperforms the other with LGBM in prediction accuracy, but the one with LGBM gets more stable predictions.

High-dimensional feature extraction of sea clutter and target signal for intelligent maritime monitoring network

As one of the source sensors of maritime intelligent traffic network, radar plays an important role in maritime monitoring and early warning. The number of features extracted by the traditional maritime radar target detection method in feature domain is small, and the sea clutter and target echo features are often not linearly separable, so the radar detection performance is seriously affected by sea clutter. To solve this problem, this paper combines the time–frequency domain processing method with the residual neural network, and uses the time–frequency transform method to improve the signal-to-clutter ratio (SCR) and the degree of difference between sea clutter and target echo. On this basis, the high-dimensional time–frequency spectrum features are extracted by using the residual neural network to improve the utilization rate of radar echo information, form a high-dimensional feature space of sea clutter and target echo that are non-linearly separable, and realize the binary classification of sea clutter and target echo. It is verified by the measured data of X-band radar that the proposed target detection method can extract the deep features of the time–frequency spectrum of radar echo, has a high classification accuracy even in the case of low SCR, and has the potential to detect weak targets in the background of strong sea clutter. In addition, the influence of different time–frequency transform methods and polarization modes on target detection performance is further analyzed. The comparative study shows that different time–frequency transform methods and polarization modes have little influence on the classification accuracy of the proposed method. In comparison, under the conditions of fractional Fourier transform and cross-polarization, the proposed method has higher classification accuracy.

Adaptive Power System Emergency Control Using Deep Reinforcement Learning

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named RLGC has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively.

High-dimensional feature extraction using bit-plane decomposition of local binary patterns for robust face recognition

Transforming an original image into a high-dimensional (HD) feature has been proven to be effective in classifying images. This paper presents a novel feature extraction method utilizing the HD feature space to improve the discriminative ability for face recognition. We observed that the local binary pattern can be decomposed into bit-planes, each of which has scale-specific directional information of the face image. Each bit-plane not only has the inherent local-structure of the face image but also has an illumination-robust characteristic. By concatenating all the decomposed bit-planes, we generate an HD feature vector with an improved discriminative ability. To reduce the computational complexity while preserving the incorporated local structural information, a supervised dimension reduction method, the orthogonal linear discriminant analysis, is applied to the HD feature vector. Extensive experimental results show that existing classifiers with the proposed feature outperform those with other conventional features under various illumination, pose, and expression variations.

High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

Purified terephthalic acid (PTA) is an important chemical raw material. P-xylene (PX) is transformed to terephthalic acid (TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to improve the product quality, as well as to visualize the fault type clearly, a fault diagnosis method based on self-organizing map (SOM) and high dimensional feature extraction method, local tangent space alignment (LTSA), is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously, and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process indicate that the LTSA–SOM can well detect and visualize the fault type.

USB: ultrashort binary descriptor for fast visual matching and retrieval.

Currently, many local descriptors have been proposed to tackle a basic issue in computer vision: duplicate visual content matching. These descriptors either are represented as high-dimensional vectors relatively expensive to extract and compare or are binary codes limited in robustness. Bag-of-visual words (BoWs) model compresses local features into a compact representation that allows for fast matching and scalable indexing. However, the codebook training, high-dimensional feature extraction, and quantization significantly degrade the flexibility and efficiency of BoWs model. In this paper, we study an alternative to current local descriptors and BoWs model by extracting the ultrashort binary descriptor (USB) and a compact auxiliary spatial feature from each keypoint detected in images. A typical USB is a 24-bit binary descriptor, hence it directly quantizes visual clues of image keypoints to about 16 million unique IDs. USB allows fast image matching and indexing and avoids the expensive codebook training and feature quantization in BoWs model. The spatial feature complementarily captures the spatial configuration in neighbor region of each keypoint, hence is used to filter mismatched USBs in a cascade verification. In image matching task, USB shows promising accuracy and nearly one-order faster speed than SIFT. We also test USB in retrieval tasks on UKbench, Oxford5K, and 1.2 million distractor images. Comparisons with recent retrieval methods manifest the competitive accuracy, memory consumption, and significantly better efficiency of our approach.

The multiple window parameter transform

The multiwindow transform, an extension of parameter transform techniques that increase performance and scope by exploiting the long-range correlated information contained in multiple portions of an image, is presented. Multiple-window transforms allow the extraction of high-dimensional features with improvement in accuracy over conventional techniques while keeping linear to low-order-polynomial computational and space requirements with respect to image size and dimensionality of the features. Using correlated information provides a direct link between extracted features and supporting regions in the image. This, coupled with evidence integration techniques, is used to suppress noisy or nonexistent feature hypotheses. Parameter spaces are implemented as constraint satisfaction networks, where feature hypotheses with overlapping support in the image compete. After an iterative relaxation phase, surviving hypotheses have disjoint support, forming a segmentation of the image. Examples show the performance and provide insight about the behavior. >