Texture-based Methods Research Articles

Artificial intelligence for computer aided detection of pneumoconiosis: A succinct review since 1974

With the increasing availability of medical imaging data and advancements in artificial intelligence (AI) and computer vision (CV) techniques, computer aided diagnostic (CAD) systems have been consistently developed to help radiologists in the detection of pneumoconiosis. Pneumoconiosis is a respiratory disease caused by long term exposure of industrial dust. Pneumoconiosis has remained prevalent, even in countries with sophisticated healthcare systems and strict workplace measures. Consequently, this review article focuses on cascading the literature of existing CAD systems for pneumoconiosis diagnosis since 1974, providing a baseline for future research in this domain. For this purpose, 58 relevant articles were first selected after employing strict inclusion criteria, through 10 reliable databases and search engines, including Scopus, IEEE, Google Scholar, and PubMed etc. This review then systematically categorizes the selected CAD studies into two patterns, based on the employed methodology for pneumoconiosis diagnosis: texture-based and non-texture-based CAD systems. This study reveals that texture-based methods have been extensively adopted for pneumoconiosis diagnosis compared to non-texture-based methods (or deep learning based). However, deep learning approaches have shown superior performance thanks to the recent availability of large annotated CXR datasets and development of deep convolutional neural network (CNN) and transformer-based architectures. Finally, the analysis is concluded with a discussion on the shortcomings of current CAD systems and some suggested future directions for the development of effective diagnostic systems. Additionally, a number of benchmark datasets have also been discussed.

A review on texture-based methods for anomaly detection in retinal optical coherence tomography images

Retina is an important body organ responsible for human vision. There are many important retinal diseases which may damage the vision and even cause blindness. Optical Coherence Tomography (OCT) is an important tool for verifying and evaluating retina. Moreover, retina is a window to brain and since the imaging from retina is simpler than brain, valuable information from brain can be obtained through retinal imaging. In fact, many neuro-degenerative diseases can be followed through retinal OCT images. Texture refers to the way of locating pixels with different intensity values in an image neighborhood. In this paper, the purpose is to provide a review on the methods which focus on the anomaly detection in OCT images. Also, the role of texture in the identification of retinal diseases is discussed in detail. Different texture descriptors in image processing applications are introduced and the methods which utilized them for the mentioned purpose are explained. In addition, different retinal diseases are classified in several classes and the texture-based methods suggested for each class of diseases are separately described.

The relationship between multiple hazards and deprivation using open geospatial data and machine learning

Deprived settlements, usually referred to as slums, are often located in hazardous areas. However, there have been very few studies to examine this notion. In this study, we leverage the advancements in open geospatial data, earth observation (EO), and machine learning to create a multi-hazard susceptibility index and a transferrable disaster risk approach to be adapted in low- and middle-income country (LMIC) cities, with low-cost methods. Specifically, we identify multi-hazards in Nairobi's selected case study area and construct a susceptibility index. Then, we test the predictability of deprived settlements using the multi-hazard susceptibility index in comparison with EO texture-based methods. Lastly, we survey 100 households in two deprived settlements (typical and atypical slums) in Nairobi and use the survey outcomes to validate the multi-hazard susceptibility index. To test the assumption that deprived areas are dominantly located in areas with higher susceptibility to multiple hazards, we contrast morphologically identified deprived settlements with non-deprived settlements. We find that deprived settlements are generally more exposed to hazards. However, there are variations between central and peripheral settlements. In testing the predictability of deprivation using multi-hazards, the multi-hazard-based model performs better for deprived settlements than for other classes. In contrast, the texture-based model is better at classifying all types of morphological settlements. Lastly, by contrasting the survey outcomes to the household interviews, we conclude that proxies used for the multi-hazard susceptibility index adequately capture the hazards. However, more localized proxies can be used to improve the index performance.

Classification of intracranial hemorrhage CT images based on texture analysis using ensemble-based machine learning algorithms: A comparative study

Blood vessels in the brain tissue can leak or burst, causing an intracranial hemorrhage, a potentially fatal disorder. One of the critical steps in brain stroke imaging is the categorization of intracranial hemorrhages. This article aims to perform texture-based intracranial hemorrhage computed tomography image classification with ensemble and machine learning classifiers using local binary pattern, local ternary pattern, and Weber local descriptor approach. The most common texture feature extraction method is a local binary pattern; however, other methods such as local ternary pattern, Weber local descriptor, and local binary pattern are also utilized for texture feature extraction. For all the extracted texture codes, histograms are applied, and finally, single feature vector texture codes by performing the concatenation of the bin. The efficacy of feature descriptors for classifying images into abnormal and normal classes was assessed using ten distinct classifiers. The result shows that Weber's local descriptor shows promising results in texture code classification for ensemble-based classification. For all texture-based methods, classification accuracy was better than standard machine learning. According to the findings of the experiment, the Random Forest classifier outperformed all other classifiers in terms of classification accuracy (86.55%), recall (86.31%), precision (87.23%), sensitivity (86.31%), specificity (86.81%), and F1-score (86.77%) for the Weber local descriptor.

GPR analysis to detect subsidence: a case study on a loaded reinforced concrete pavement

ABSTRACT Subsidence seriously affects the structural stability and safety of pavements and foundation soils. In heavy-loaded pavements, there is a risk of floor sinking and further construction collapse; hence, there is a need to develop efficient methodologies to detect subsidence earlier. This work proposes the use of ground penetrating radar (GPR) as a solution to non-invasively inspect the subsoil. Furthermore, as the interpretation of the GPR data is arguably subjective and highly dependent on who interprets it, different imaging techniques are herein exploited to improve the interpretability and detection of subsidence and settlement phenomena. The approach was applied to a heavily loaded reinforced concrete pavement servicing a manufacturing facility. Amplitude- and texture-based imaging methods were used to detect subsidence. The interpretation of such imaging was validated with additional geotechnical studies, which show that the proposed methods provide reliable results with good agreement between techniques.

Anatomical texture patterns identify cerebellar distinctions between essential tremor and Parkinson's disease.

Voxel‐based morphometry is an established technique to study focal structural brain differences in neurologic disease. More recently, texture‐based analysis methods have enabled a pattern‐based assessment of group differences, at the patch level rather than at the voxel level, allowing a more sensitive localization of structural differences between patient populations. In this study, we propose a texture‐based approach to identify structural differences between the cerebellum of patients with Parkinson's disease (n = 280) and essential tremor (n = 109). We analyzed anatomical differences of the cerebellum among patients using two features: T1‐weighted MRI intensity, and a texture‐based similarity feature. Our results show anatomical differences between groups that are localized to the inferior part of the cerebellar cortex. Both the T1‐weighted intensity and texture showed differences in lobules VIII and IX, vermis VIII and IX, and middle peduncle, but the texture analysis revealed additional differences in the dentate nucleus, lobules VI and VII, vermis VI and VII. This comparison emphasizes how T1‐weighted intensity and texture‐based methods can provide a complementary anatomical structure analysis. While texture‐based similarity shows high sensitivity for gray matter differences, T1‐weighted intensity shows sensitivity for the detection of white matter differences.

Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin.

Simple SummaryThe appearance of histology images stained with H&E can vary a lot as a consequence of changes in the reagents, staining conditions, preparation procedure and acquisition system. In this work we investigated whether color preprocessing—specifically color deconvolution and color normalization—could be used to correct such variability and improve the performance of automated classification procedures. Experimenting on 11 datasets, 13 image descriptors and eight color pre-processing methods we found that doing no color preprocessing was the best option in most cases.Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature—for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers—specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.

Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping.

In computational pathology, automated tissue phenotyping in cancer histology images is a fundamental tool for profiling tumor microenvironments. Current tissue phenotyping methods use features derived from image patches which may not carry biological significance. In this work, we propose a novel multiplex cellular community-based algorithm for tissue phenotyping integrating cell-level features within a graph-based hierarchical framework. We demonstrate that such integration offers better performance compared to prior deep learning and texture-based methods as well as to cellular community based methods using uniplex networks. To this end, we construct celllevel graphs using texture, alpha diversity and multi-resolution deep features. Using these graphs, we compute cellular connectivity features which are then employed for the construction of a patch-level multiplex network. Over this network, we compute multiplex cellular communities using a novel objective function. The proposed objective function computes a low-dimensional subspace from each cellular network and subsequently seeks a common low-dimensional subspace using the Grassmann manifold. We evaluate our proposed algorithm on three publicly available datasets for tissue phenotyping, demonstrating a significant improvement over existing state-of-the-art methods.

Pattern Statistics Network for Classification of High-Resolution SAR Images

The classification of synthetic aperture radar (SAR) images is of great importance for rapid scene understanding. Recently, convolutional neural networks (CNNs) have been applied to the classification of single-polarized SAR images. However, it is still difficult due to the random and complex spatial patterns lying in SAR images, especially in the case of finite training data. In this paper, a pattern statistics network (PSNet) is proposed to address this problem. PSNet borrows the idea from the statistics and probability theory and explicitly embeds the random nature of SAR images in the representation learning. In the PSNet, both fluctuation and pattern representations are extracted for SAR images. More specifically, the fluctuation representation does not consider the rigorous relationships between local pixels and only describes the average fluctuation of local pixels. By contrast, the pattern representation is devoted to hierarchically capturing the interactions between local pixels, namely, the spatial patterns of SAR images. The proposed PSNet is evaluated on three real SAR data, including spaceborne and airborne data. The experimental results indicate that the fluctuation representation is useful and PSNet achieves superior performance in comparison with related CNN-based and texture-based methods.

Use of Texture Feature Maps for the Refinement of Information Derived from Digital Intraoral Radiographs of Lytic and Sclerotic Lesions

The aim of this study was to examine whether additional digital intraoral radiography (DIR) image preprocessing based on textural description methods improves the recognition and differentiation of periapical lesions. (1) DIR image analysis protocols incorporating clustering with the k-means approach (CLU), texture features derived from co-occurrence matrices, first-order features (FOF), gray-tone difference matrices, run-length matrices (RLM), and local binary patterns, were used to transform DIR images derived from 161 input images into textural feature maps. These maps were used to determine the capacity of the DIR representation technique to yield information about the shape of a structure, its pattern, and adequate tissue contrast. The effectiveness of the textural feature maps with regard to detection of lesions was revealed by two radiologists independently with consecutive interrater agreement. (2) High sensitivity and specificity in the recognition of radiological features of lytic lesions, i.e., radiodensity, border definition, and tissue contrast, was accomplished by CLU, FOF energy, and RLM. Detection of sclerotic lesions was refined with the use of RLM. FOF texture contributed substantially to the high sensitivity of diagnosis of sclerotic lesions. (3) Specific DIR texture-based methods markedly increased the sensitivity of the DIR technique. Therefore, application of textural feature mapping constitutes a promising diagnostic tool for improving recognition of dimension and possibly internal structure of the periapical lesions.

Integrating Feature Extractors for the Estimation of Human Facial Age

ABSTRACTFacial feature extraction algorithms play an important role in many applications of face biometrics such as face recognition for person identification, classification of emotions by facial expression recognition and age estimation using facial images. In this paper, an integration of different type of feature extraction algorithms is applied on facial images for accurate age estimation. This integration is performed by using two-level fusion of features and scores with the help of feature-level and score-level fusion techniques. In our proposed method, the advantage of using different types of features such as biologically inspired features, texture-based features, and appearance-based features is used. Feature-level fusion of biologically inspired and texture-based methods is integrated into the proposed method and their combination is fused with an appearance-based method using score-level fusion. Experiments on the publicly available MORPH and FG-NET databases prove the effectiveness of the proposed method and the proposed method outperforms many of the state-of-the-art systems.

Statistics Learning Network Based on the Quadratic Form for SAR Image Classification

The convolutional neural network (CNN) has shown great potential in many fields; however, transferring this potential to synthetic aperture radar (SAR) image interpretation is still a challenging task. The coherent imaging mechanism causes the SAR signal to present strong fluctuations, and this randomness property calls for many degrees of freedom (DoFs) for the SAR image description. In this paper, a statistics learning network (SLN) based on the quadratic form is presented. The statistical features are expected to be fitted in the SLN for SAR image representation. (i) Relying on the quadratic form in linear algebra theory, a quadratic primitive is developed to comprehensively learn the elementary statistical features. This primitive is an extension to the convolutional primitive that involves both nonlinear and linear transformations and provides more flexibility in feature extraction. (ii) With the aid of this quadratic primitive, the SLN is proposed for the classification task. In the SLN, different types of statistics of SAR images are automatically extracted for representation. Experimental results on three datasets show that the SLN outperforms a standard CNN and traditional texture-based methods and has potential for SAR image classification.

Vehicles detection for illumination changes urban traffic scenes employing adaptive local texture feature background model

An Adaptive Local Texture Feature Background Model (ALTF-BM) is proposed to resolve the deficiency in current background models, which are easily contaminated by sudden and gradual illumination changes in complex urban traffic scenes. Based on Weber's law, the authors first develop Adaptive Local Texture Feature (ALTF), calculated over a predefined local region around the pixel employing an adaptive distance threshold, and then the background is modelled on the base of sample consensus scheme using the calculated features. Furthermore, to label the foreground pixels, the difference between the background model and input video frames is then directly compared by ALTF encoding. Finally, the model is updated using the random update policy to adapt to the changing illumination and the dynamic background. The experimental results on real-world urban traffic videos and the public Change Detection benchmark of 2014 (CDnet2014) show that the proposed ALTF-BM offers the best performance compared to the other state-of-the-art texture-based methods, and the average F -measures and similarity results of the proposed ALTF-BM are 0.547 and 0.393 higher than benchmarks on the night traffic-light sequence, respectively. The encouraging experimental results demonstrate the efficiency of the proposed ALTF-BM in handling sudden and gradual illumination changes in urban traffic scenes.

EFFICIENT AND ACCURATE FUSION OF MASSIVE VECTOR DATA ON 3D TERRAIN

Abstract. This paper presents a viewpoint-related fusion method of massive vector data and 3D terrain, in order to superpose the massive 2D vector data onto the undulating multi-resolution 3D terrain precisely and efficiently. First, the method establishes an adaptive hierarchical grid spatial index for vector data. It will determine the geographic spatial relationship between vector data and the tiles of 3D terrain in the visible area; secondly, this method will use the improved sub-pixel graphics engine AggExt to generate textures for vector data that has been bound to terrain tiles in real time; Finally, considering that a large amount of vector data will generate a lot of 2D textures in the computer memory, the method should release the “expired” vector textures. In this paper, in order to take into account the real-time convergence and the smooth interactivity of 3D scenes, this method will adopt a multi-threading strategy. The experimental results show that this method can realize the real-time and seamless fusion of massive vector objects on the 3D terrain, and has a high rendering frame rate. It can also reduce the aliasing produced by traditional texture-based methods and improve the quality of vector data fusion.

On the generalization of color texture-based face anti-spoofing

Despite the significant attention given to the problem of face spoofing, we still lack generalized presentation attack detection (PAD) methods performing robustly in practical face recognition systems. The existing face anti-spoofing techniques have indeed achieved impressive results when trained and evaluated on the same database (i.e. intra-test protocols). Cross-database experiments have, however, revealed that the performance of the state-of-the-art methods drops drastically as they fail to cope with new attacks scenarios and other operating conditions that have not been seen during training and development phases. So far, even the popular convolutional neural networks (CNN) have failed to derive well-generalizing features for face anti-spoofing. In this work, we explore the effect of different factors, such as acquisition conditions and presentation attack instrument (PAI) variation, on the generalization of color texture-based face anti-spoofing. Our extensive cross-database evaluation of seven color texture-based methods demonstrates that most of the methods are unable to generalize to unseen spoofing attack scenarios. More importantly, the experiments show that some facial color texture representations are more robust to particular PAIs than others. From this observation, we propose a face PAD solution of attack-specific countermeasures based solely on color texture analysis and investigate how well it generalizes under display and print attacks in different conditions. The evaluation of the method combining attack-specific detectors on three benchmark face anti-spoofing databases showed remarkable generalization ability against display attacks while print attacks require still further attention.

Using textural analysis for regional landform and landscape mapping, Eastern Guiana Shield

Availability of digital elevation models (DEM) of increased spatial resolution has triggered interest in texture-based methods for automated geomorphometry. This prospect is all the more appealing concerning tropical countries for which mapping of geomorphic entities has remained limited despite its relevance for natural resource assessment and land planning. In this paper we applied to the Shuttle Radar Topography Mission (SRTM) DEM a multiscale texture analysis based on Fourier 2D periodogram (FOTO method) to reach a spatially coherent mapping that would agree with current knowledge over the conterminous territories of French Guiana and Amapa (Brazil). Through a principal component analysis, FOTO-extracted textural features were found complementary and combined with six common physiographic criteria (convexity, wetness index, dissection, elevation, and percent of steep and low slopes) calculated from the DEM. This provided a primary semiautomated mapping of 16 classes of landform types. To increase legibility, we aggregated into eight landscape unit classes that were the most similar in terms of texture and relief criteria while being frequently contiguous. The resulting map was compared with existing maps that were, however, more local or of lesser resolution and informative content over the region of interest. Multiscale Fourier spectra proved useful to depict land surface texture and, in synergy with relief variables, allowed identifying a large variety of landforms for broad-scale geomorphic mapping in the humid tropics.

A Generative Model for Volume Rendering.

We present a technique to synthesize and analyze volume-rendered images using generative models. We use the Generative Adversarial Network (GAN) framework to compute a model from a large collection of volume renderings, conditioned on (1) viewpoint and (2) transfer functions for opacity and color. Our approach facilitates tasks for volume analysis that are challenging to achieve using existing rendering techniques such as ray casting or texture-based methods. We show how to guide the user in transfer function editing by quantifying expected change in the output image. Additionally, the generative model transforms transfer functions into a view-invariant latent space specifically designed to synthesize volume-rendered images. We use this space directly for rendering, enabling the user to explore the space of volume-rendered images. As our model is independent of the choice of volume rendering process, we show how to analyze volume-rendered images produced by direct and global illumination lighting, for a variety of volume datasets.

Face Identification Using LBP&LDA Methods

In this paper, we have evaluated the possibility of using database for the task of driver identification in near-ir videos using face recognition techniques. Two texture-based methods are presented to solve this task. The first method utilizes local binary patterns (lbp) combined with local discriminant analysis (lda) to identify subjects while the second method investigates bruteforce template matching for identification purposes. The lbp-method is fast while the template matching method proved to be very time-consuming. Future refinements and extensions are proposed for both methods.

Spoof detection on face and palmprint biometrics

Spoofing attacks made by non-real images are a major concern to biometric systems. This paper presents a novel solution for distinguishing between live and forged identities using the fusion of texture-based methods and image quality assessment measures. In our approach, we used LBP and HOG texture descriptors to extract texture information of an image. Additionally, feature space of seven full-reference complementary image quality measures is considered including peak signal-to-noise ratio, structural similarity, mean-squared error, normalized cross-correlation, maximum difference, normalized absolute error and average difference. We built a palmprint spoof database made by printed palmprint photograph of PolyU palmprint database using camera. Experimental results on three public-domain face spoof databases (Idiap Print-Attack, Replay-Attack and MSU MFSD) and palmprint spoof database show that the proposed solution is effective in face and palmprint spoof detection.

Texture feature benchmarking and evaluation for historical document image analysis

The use of different texture-based methods is pervasive in different subfields and tasks of document image analysis (DIA) and particularly in historical DIA (HDIA). Nevertheless, faced with a large diversity of texture-based methods used for HDIA, few questions arise. Which texture methods are firstly well suited for segmenting graphical contents from textual ones, discriminating various text fonts and scales, and separating different types of graphics? Then, which texture-based method represents a constructive compromise between the performance and the computational cost? Thus, in this article a benchmarking of the most classical and widely used texture-based feature sets has been conducted using a classical texture-based pixel-labeling scheme on a large corpus of historical documents to have satisfactory and clear answers to the above questions. We focus on determining the performance of each texture-based feature set according to the document content. The results reported in this study provide firstly a qualitative measure of which texture-based feature sets are the most appropriate and secondly a useful benchmark in terms of performance and computational cost for current and future research efforts in HDIA.