Graph-based Feature Extraction Research Articles

Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques

Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions.

Explainable Image Recognition With Graph-Based Feature Extraction

Explainable Image Recognition With Graph-Based Feature Extraction

Enhancing Lithofacies Interpretation in Well Logs With Graph-Based Feature Extraction

Enhancing Lithofacies Interpretation in Well Logs With Graph-Based Feature Extraction

Synergistic Detection of Multimodal Fake News Leveraging TextGCN and Vision Transformer

In today's digital age, the rapid spread of fake news is a pressing concern. Fake news, whether intentional or inadvertent, manipulates public sentiment and threatens the integrity of online information. To address this, effective detection and prevention methods are vital. Detecting and addressing this multimodal fake news is an intricate challenge as, unlike traditional news articles that predominantly rely on textual content, multimodal fake news leverages the persuasive power of visual elements, making its identification a formidable task. Manipulated images can significantly sway individuals' perceptions and beliefs, making the detection of such deceptive content complex. Our research introduces an innovative approach to multimodal fake news identification by presenting a fusion-based methodology that harnesses the capabilities of Text Graph Convolutional Neural Networks (TextGCN) and Vision Transformers (ViT) to effectively utilise both text and image modalities. The proposed Methodology starts with preprocessing textual content using TextGCN, allowing for the capture of intricate structural dependencies among words and phrases. Simultaneously, visual features are extracted from associated images using ViT. Through a fusion mechanism, these modalities seamlessly integrate, yielding superior embeddings. The primary contributions encompass an in-depth exploration of multimodal fake news detection through a fusion-based approach. What sets our approach apart from existing techniques is its integration of graph-based feature extraction through TextGCN. While previous methods predominantly rely on text or image features, our approach harnesses the additional semantic information and intricate relationships within a graph structure, in addition to image embeddings. This enables our method to capture more comprehensive understanding of the data, resulting in increased accuracy and reliability. Our experiments demonstrate the exceptional performance of our fusion-based approach, which leverages multiple modalities and incorporates graph-based representations and semantic relationships. This method outperformed single modalities of text or image, achieving an impressive accuracy of 94.17% using a neural network after fusion. By seamlessly integrating graph-based representations and semantic relationships, our fusion-based technique represents a significant stride in addressing the challenges posed by multimodal fake news.

Graph-based feature extraction on object-centric event logs

Process mining techniques have proven crucial in identifying performance and compliance issues. Traditional process mining, however, is primarily case-centric and does not fully capture the complexity of real-life information systems, leading to a growing interest in object-centric process mining. This paper presents a novel graph-based approach for feature extraction from object-centric event logs. In contrast to established methods for feature extraction from traditional event logs, object-centric logs present a greater challenge due to the interconnected nature of events related to multiple objects. This paper addresses this gap by proposing techniques and tools for feature extraction specifically designed for object-centric event logs. In this work, we focus on features pertaining to the lifecycle of the objects and their interaction. These features enable a more comprehensive understanding of the process and its inherent complexities. We demonstrate the applicability of our approach through its implementation in two significant areas: anomaly detection and throughput time prediction for objects in the process. Our results, based on four problems in a Procure-to-Pay process, affirm the potential of our proposed features in enhancing the scope of process mining. By effectively transforming object-centric event logs into numeric vectors, we pave the way for the application of a broader range of machine learning techniques, such as classification, prediction, clustering, and anomaly detection, thereby extending the capabilities of process mining.

A dynamical graph-based feature extraction approach to enhance mental task classification in brain–computer interfaces

Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain–computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics. Moreover, existing graph theoretic approaches are mostly used to reveal the topological patterns of brain functional networks based on synchronization between signals from distinctive spatial regions, instead of interdependence between states at different timestamps. In this study, we present a robust fold-wise hyperparameter optimization framework utilizing a series of conventional graph-based measurements combined with spectral graph features and investigate its discriminative performance on classification of a designed mental task in 6 participants with amyotrophic lateral sclerosis (ALS). Across all of our participants, we reached an average accuracy of 71.1%±4.5% for mental task classification by combining the global graph-based measurements and the spectral graph features, higher than the conventional non-graph based feature performance (67.1%±7.5%). Compared to using either one of the graphic features (66.3%±6.5% for the eigenvalues and 65.9%±5.2% for the global graph features), our feature combination strategy shows considerable improvement in both accuracy and robustness performance. Our results indicate the feasibility and advantage of the presented fold-wise optimization framework utilizing graph-based features in BCI systems targeted at end-users.

A new analysis of flow noise outside the time-frequency representation using graph-based feature extraction

Among various strategies to analyze spatiotemporal aero-hydro acoustic field, complex networks are a novel technique to deal with the acoustic pressure signals (APSs) which can fully reflect the inherent structure of the time series into complex networks. In this study first, sound generated from a low Mach number and turbulent flow around a three-dimensional circular cylinder is simulated. Then, using the complex network algorithm, APSs are transformed into natural visibility graphs (NVGs), and, their hidden signatures (macroscopic and microscopic characteristics) are unveiled using the topological metrics of the NVG. Later, spatial variations of 11 network metrics associated with 76 receiver signals distributed around the cylinder are explored and a bridge is built between these variations and their physical interpretations. The results show that all network properties corresponding to receiver signals located at a constant angular position are independent of the radial direction. Moreover, irrespective of the APSs properties, the power-law degree distribution is a feature shared by all networks, suggesting that it is a universal characteristic and highlights systems' self-organization and self-similarity characteristics in the sound field. Finally, physical interpretation of graph structures indicates that graph-based feature extraction enables identifying, distinguishing, and exploring underlying sound field dynamics in more detail.

A Dynamical Graph-Based Feature Extraction Approach to Enhance Mental Task Classification in Brain-Computer Interfaces

A Dynamical Graph-Based Feature Extraction Approach to Enhance Mental Task Classification in Brain-Computer Interfaces

Sparsity-Induced Graph Convolutional Network for Semisupervised Learning

The graph representation (GR) in a data space reveals the intrinsic information as well as the natural relationships of data, which is regarded as a powerful means of representation for solving the semisupervised learning problem. To effectively learn on a predefined graph with both labeled data and unlabeled data, the graph convolutional network (GCN) was proposed and has attracted a lot of attention due to its high-performance graph-based feature extraction along with its low computational complexity. Nevertheless, the performance of GCNs is highly sensitive to the quality of the graph, meaning with high probability, the GCNs will achieve poor performances on a badly defined graphs. In numerous real-world semisupervised learning problems, the graph connecting each entity in the data space implicitly exists so that there is no naturally predefined graph in these problems. To overcome the issues, in this article, we apply unified GR techniques and GCNs in a framework that can be implemented in semisupervised learning problems. To achieve this framework, we propose sparsity-induced graph convolutional network (SIGCN) for semisupervised learning. SIGCN introduces the sparsity to formulate significant relationships between instances by constructing a newly proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_0$</tex-math></inline-formula> -based graph (termed as the sparsity-induced graph) before applying graph convolution to capture the high-quality features based on this graph for label propagation. We prove and demonstrate the feasibility of the unified framework as well as effectiveness in capturing features. Extensive experiments and comparisons were performed to show that the proposed SIGCN obtains a state-of-the-art performance in the semisupervised learning problem.

Graph-based feature extraction and classification of wet and dry cough signals: a machine learning approach.

This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.

SuperGraph: Spatial-Temporal Graph-Based Feature Extraction for Rotating Machinery Diagnosis

Vibration signals always contain noise and irregularities, which makes spectrum analysis difficult to extract high-level features. Recently, graph theory has been applied to spectrum analysis to improve the performance of feature extraction. By converting the raw data into graphs, hidden structural and topological information can be obtained. In this article, a spatial-temporal graph-based feature extraction, called SuperGraph, for rotating machinery fault diagnosis is proposed. Specifically, graph theory-based spectrum analysis is used to construct the spatial-temporal graph. Then, the Laplacian matrix-based feature vector is extracted from the constructed spatial-temporal graph. By this means, the spatial-temporal graph is converted into the one-dimensional (1-D) vector for further constructing SuperGraph, where each node of the SuperGraph represents a spatial-temporal graph and the SuperGraph is composed of many local graphs. In the local graph, only the same type of nodes are connected to form a fully connected graph. Thus, the task of graph classification can be transformed into classifying the nodes in the SuperGraph. After graph convolutional network is established for learning and obtaining deep features, the label of nodes is identified from a <inline-formula><tex-math notation="LaTeX">$softmax$</tex-math></inline-formula> model. Experiments are conducted on two benchmarking datasets and a practical experimental platform to verify effectiveness of the proposed fault diagnosis method.

Graph-based feature extraction: A new proposal to study the classification of music signals outside the time-frequency domain.

Most feature extraction algorithms for music audio signals use Fourier transforms to obtain coefficients that describe specific aspects of music information within the sound spectrum, such as the timbral texture, tonal texture and rhythmic activity. In this paper, we introduce a new method for extracting features related to the rhythmic activity of music signals using the topological properties of a graph constructed from an audio signal. We map the local standard deviation of a music signal to a visibility graph and calculate the modularity (Q), the number of communities (Nc), the average degree (〈k〉), and the density (Δ) of this graph. By applying this procedure to each signal in a database of various musical genres, we detected the existence of a hierarchy of rhythmic self-similarities between musical styles given by these four network properties. Using Q, Nc, 〈k〉 and Δ as input attributes in a classification experiment based on supervised artificial neural networks, we obtained an accuracy higher than or equal to the beat histogram in 70% of the musical genre pairs, using only four features from the networks. Finally, when performing the attribute selection test with Q, Nc, 〈k〉 and Δ, along with the main signal processing field descriptors, we found that the four network properties were among the top-ranking positions given by this test.

Graph based feature extraction and hybrid classification approach for facial expression recognition

In the current trends, face recognition has a remarkable attraction towards favorable and inquiry of an image. Several algorithms are utilized for recognizing the facial expressions, but they lack in the issues like inaccurate recognition of facial expression. To overcome these issues, a Graph-based Feature Extraction and Hybrid Classification Approach (GFE-HCA) is proposed for recognizing the facial expressions. The main motive of this work is to recognize human emotions in an effective manner. Initially, the face image is identified using the Viola–Jones algorithm. Subsequently, the facial parts such as right eye, left eye, nose and mouth are extracted from the detected facial image. The edge-based invariant transform feature is utilized to extract the features from the extracted facial parts. From this edge-based invariant features, the dimensions are optimized using Weighted Visibility Graph which produces the graph-based features. Also, the shape appearance-based features from the facial parts are extracted. From these extracted features, facial expressions are recognized and classified using a Self-Organizing Map based Neural Network Classifier. The performance of this GFE-HCA approach is evaluated and compared with the existing techniques, and the superiority of the proposed approach is proved with its increased recognition rate.

Structured optimal graph based sparse feature extraction for semi-supervised learning

Graph-based feature extraction is an efficient technique for data dimensionality reduction, and it has gained intensive attention in various fields such as image processing, pattern recognition, and machine learning. However, conventional graph-based dimensionality reduction algorithms usually depend on a fixed weight graph called similarity matrix, which seriously affects the subsequent feature extraction process. In this paper, a novel structured optimal graph based sparse feature extraction (SOGSFE) method for semi-supervised learning is proposed. In the proposed method, the local structure learning, sparse representation, and label propagation are simultaneously framed to perform data dimensionality reduction. In particular the similarity matrix and the projection matrix are obtained by an iterative calculation manner. The experimental results on several public image datasets demonstrate the robustness and effectiveness of the proposed method.

A correntropy-based classifier for motor imagery brain-computer interfaces

Objective. This work aims to present a deeper investigation of the classification performance achieved by a motor imagery (MI) EEG-based brain-computer interface (BCI) using functional connectivity (FC) measures as features. The analysis is performed for two different datasets and analytical setups, including an information-theoretic based FC estimator (correntropy). Approach. In the first setup, using data acquired by our group, correntropy was compared to Pearson and Spearman correlations for FC estimation followed by graph-based feature extraction and two different classification strategies—linear discriminant analysis (LDA) and extreme learning machines (ELMs) - coupled with a wrapper for feature selection in the mu (7-13 Hz) and beta (13-30 Hz) frequency bands. In the second setup, the BCI competition IV dataset 2a was considered for a broader comparison. Main results. For our own database the correntropy / degree centrality / ELM approach resulted in the most solid framework, with overall classification error as low as 5%. When using the BCI competition dataset, our best result provided a performance comparable to those of the top three competitors. Significance. Correntropy was shown to be the best FC estimator in all analyzed situations in the first experimental setup, capturing the signal temporal behavior and being less sensitive to outliers. The second experimental setup showed that the inclusion of different frequency bands can bring more information and improve the classification performance. Finally, our results pointed towards the importance of the joint use of different graph measures for the classification.

Graph Fusion for Finger Multimodal Biometrics

In terms of biometrics, a human finger itself is with trimodal traits including fingerprint, finger-vein, and finger-knuckle-print, which provides convenience and practicality for finger trimodal fusion recognition. The scale inconsistency of finger trimodal images is an important reason affecting effective fusion. It is therefore very important to developing a theory of giving a unified expression of finger trimodal features. In this paper, a graph-based feature extraction method for finger biometric images is proposed. The feature expression based on graph structure can well solve the problem of feature space mismatch for the finger three modalities. We provide two fusion frameworks to integrate the finger trimodal graph features together, the serial fusion and coding fusion. The research results can not only promote the advancement of finger multimodal biometrics technology but also provide a scientific solution framework for other multimodal feature fusion problems. The experimental results show that the proposed graph fusion recognition approach obtains a better and more effective recognition performance in finger biometrics.

Joint sparse representation and locality preserving projection for feature extraction

Traditional graph-based feature extraction methods use two separated procedures, i.e., graph learning and projection learning to perform feature extraction. They make the feature extraction result highly dependent on the quality of the initial fixed graph, while the graph may not be the optimal one for feature extraction. In this paper, we propose a novel unsupervised feature extraction method, i.e., joint sparse representation and locality preserving projection (JSRLPP), in which the graph construction and feature extraction are simultaneously carried out. Specifically, we adaptively learn the similarity matrix by sparse representation, and at the same time, learn the projection matrix by preserving local structure. Compared with traditional feature extraction methods, our approach unifies graph learning and projection learning to a common framework, thus learns a more suitable graph for feature extraction. Experiments on several public image data sets demonstrate the effectiveness of our proposed algorithm.

Computerized Histologic Image-Based Risk Score (IbRiS) Classifier for ER+ Breast Cancer.

Abstract Background: Measurement of estrogen receptor (ER) expression is a routine part of clinical evaluation of individual breast cancers and is used to guide treatment. However not all ER+ breast cancers show equal benefit from hormonal or other treatment. The RT-PCR based Oncotype Dx assay, has been recently shown to robustly stratify early stage ER+ breast cancer and identify those tumors that will have low recurrence rates when treated with adjuvant hormonal therapy alone. Interestingly, standard pathologic grading, based on visual analysis of tumor morphology by trained pathologists, has a strong correlation with Oncotype Dx recurrence scores; low recurrence tumors are mostly low grade, and high recurrence tumors are mostly high grade. However, a major problem with use of pathologist-assigned histologic grade as a prognostic tool is the lack of reproducibility of histological grading between different pathologists. Computer aided image analysis and machine learning techniques offer a way to obtain highly reproducible image-based classification of ER+ breast cancer. These analyses can be performed on digital images of routinely obtained breast cancer histology and be incorporated into a prognostic assay.Materials and Methods: High resolution digital images were obtained for a series of ER+ breast cancers for which associated Oncotype Dx Assay results were available. Regions of invasive breast cancer were identified and then processed by computer-assisted image analysis methods. An automated nuclear detection scheme based on Expectation Maximization algorithm was used to identify cancer cell nuclei, followed by graph-based feature extraction using the Voronoi graph, Delaunay Triangulation and Minimum Spanning Trees, and dimensionality reduction using Graph Embedding with Support Vector Machine based classification. The final manifold generated by GE was unwrapped into a linear space and a Euclidean distance metric was used to generate a single score (Image Based Risk Score- (IbRiS)) for each sample. Correlations between IbRS, clinical features such as grade, and Oncotype Dx Recurrance Score were determined.Results: Unsupervised analysis of image-based features of high resolution digital images of ER+ breast cancer histology leads to natural separation of tumors, with low grade tumors separating from high grade tumors. There is also a robust separation of tumors with high Oncotype Dx Recurrance Scores from tumors with low Recurrance Scores.Discussion: Unsupervised analysis of high resolution image-based features can stratify ER+ breast cancers in a fashion that correlates well with a gene-expression based prognostic assay, Oncotype Dx. These data suggest that tumors with distinct gene-expression profiles also have distinct image-based features that can be measured by computer-aided image analysis and used to build prognostic and predictive assays. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3046.