Multiresolution Texture Research Articles

Robust Palm-print Recognition Using Multi-resolution Texture Patterns with Artificial Neural Network

Robust Palm-print Recognition Using Multi-resolution Texture Patterns with Artificial Neural Network

Multiresolution texture analysis of histopathologic images using ecological diversity measures

Multiresolution texture analysis of histopathologic images using ecological diversity measures

Mixture of multivariate generalized Gaussians for multi-band texture modeling and representation

We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables to build a compact and accurate representation of texture images using multi-resolution texture transforms. The MoMGG model enables to describe the joint statistics of subbands in different scales and orientations, as well as between adjacent locations within the same subband, providing a precise description of the texture layout. It can also combine different multi-scale transforms to build a richer and more representative texture signature for image similarity measurement. We tested our model on both traditional texture transforms (e.g., wavelets, contourlets, maximum response filter) and convolution neural networks (CNNs) features (e.g., ResNet, SqueezeNet). Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-the-art methods.

Classification of colored spun fabric structure based on wavelet decomposition and hierarchical hybrid classifier

In view of the difficulty in extracting the feature parameters of colored spun fabric structure, this article proposes an automatic classification algorithm based on wavelet decomposition and hierarchical hybrid classifier. Based on HSV color space, the algorithm uses the wavelet function to perform multi-resolution decomposition and parameter feature extraction of colored spun fabric image. At the same time, it uses BP network and naive Bayes to build hierarchical hybrid classifier, and realizes the classification of the structure feature parameters. The experimental results show that the extracted fabric weave features not only have the ability of multi-resolution texture information representation, but also the constructed hybrid classifier has the characteristics of cascade enhancement. The average classification accuracy of 176 colored spun fabric structure with different dyeing fiber blending coefficient and twist coefficient is 96.67%, which proves the effectiveness and robustness of the method. The research in this article has important guiding significance to construct stable and effective colored spun fabric design and production system.

Intra-Vascular Ultra Sound Image Classification System for the Diagnosis of Coronary Artery Disease Using Adaptive Wavelet Transform with Support Vector Machine

In this study, an efficient Decision Support System (DSS) is presented to classify coronary artery disease using Intra-Vascular Ultra Sound (IVUS) images. IVUS images are commonly used to diagnose coronary artery diseases. Wavelet transform is a multiresolution texture analysis tool which is applied to various image analysis and classification systems. Unlike the wavelet transform, Empirical Wavelet Transform (EWT) is a dependent decomposition approach that provides superior temporal and frequency information. Hence, EWT is considered as a feature extraction approach in this study. Before extracting EWT features, an adaptive non-linear speckle reducing filter; Lee filter is used to remove the IVUS images’ noises. The accumulated energies of EWT sub-bands are computed and fed to four Support Vector Machine (SVM) for coronary plague classification into five different classes; normal, calcium, fibrous, necrotic (thrombus) and soft plague (fibro-fatty). A total number of 400 IVUS images and their corresponding labeling are obtained from Shifa hospitals, Tirunelveli, Tamilnadu, India. Results prove that the classification of coronary plague is done with higher accuracy by using the EWT-SVM approach.

Radiological image retrieval technique using multi-resolution texture and shape features

Medical image analysis plays a very indispensable role in providing the best possible medical support to a patient. With the rapid advancements in modern medical systems, these digital images are growing exponentially and reside in discrete places. These images help a medical practitioner in understanding the problem and then the best suitable treatment. Radiological images are very often found to be the critical constituent of medical images. So, in health care, manual retrieval of visually similar images becomes a very tedious task. To address this issue, we have suggested a content-based medical image retrieval (CBMIR) system that effectively analyzes a Radiological image’s primitive visual features. Since radiological images are in gray-scale form, these images contain rich texture and shape features only. So, we have suggested a novel multi-resolution radiological image retrieval system that uses texture and shape features for content analysis. Here, we have employed a multi-resolution modified block difference of inverse probability (BDIP) and block-level variance of local variance (BVLC) for shape and texture features, respectively. Our proposed scheme uses a multi-resolution and variable window size feature extraction strategy to maintain the block-level co-relation and extract more salient visual features. Further, we have used the MURA x-ray image dataset, which has 40561 images captured from 12173 different patients to demonstrate the proposed scheme’s retrieval performance. We have also performed and compared image retrieval experiments on Brodatz and STex texture, Corel-1K, and GHIM-10K natural image datasets to demonstrate the robustness and improvement over other contemporaries.

Urban change detection analysis utilizing multiresolution texture features from polarimetric SAR images

This paper presents a multiresolution textural approach to change detection in multitemporal polarimetric synthetic aperture radar images. Several change detection methods utilizing wavelet based features from remotely sensed images have been developed previously. The aim of this paper is to investigate and propose a texture based change detection method that applies curvelet and contourlet transforms on polarimetric synthetic aperture radar (SAR) images. The proposed approach exploits curvelet and contourlet based multiscale decomposition of original images where textural information is extracted at various scales and in different directions in terms of statistical moments and energy to generate feature maps. The L1-norm is used in the proposed method to generate the difference image, which is thresholded using the maximum entropy principle to obtain the final change detection map. The results are compared with the changes detected by the wavelet based textural features, plain texture difference, image difference and log ratio methods. Finally, accuracy assessment and comparative analysis are performed for change maps carried out to review missed changes, false-alarms and the overall accuracy of this method. The results are also analysed statistically with area under ROC curve (AUC). The study found that the proposed method produces results with high change detection accuracy with better edge continuity and greater AUC.

Evaluation of discrete orthogonal versus polar Stockwell Transform for local multi-resolution texture analysis using brain MRI of multiple sclerosis patients

The Stockwell Transform has the potential to perform multi-resolution texture analysis in magnetic resonance imaging (MRI). However, it is computationally intensive and memory demanding. The polar Stockwell Transform (PST) is rotation-invariant and relatively memory efficient, but still computationally demanding. The new Discrete Orthogonal Stockwell Transform (DOST) appears to have addressed both the computation and storage challenges; however, its utility in localized texture analysis remains unclear. Our goal was to investigate the theory and texture analysis ability of the DOST versus PST using both synthetic and MR images, and explore the relative importance of the associated texture features using a simple classification example based on clinical brain MRI of six multiple sclerosis patients. MRI texture analysis focused on FLAIR images, and the classification used a machine learning algorithm, random forest, that differentiated regions of interest (ROIs) into 2 classes: white matter lesions, and the contralateral normal-appearing white matter (control). Our results showed that the PST features had a greater ability in detecting subtle changes in image structure than the DOST and polar-index DOST (PDOST). Quantitatively, based on 187 lesion and 187 control ROIs, both the PST and the rotation-invariant radial PST performed better in the classification than the DOST and PDOST, where the latter were no better than guessing (p = 0.65 and 0.98). Further analysis using a hierarchical random forest showed that combining MRI signal intensity with the PST or DOST predictions increased the classification performance, with the accuracy, sensitivity, and specificity all improved to >85% in the tests. Collectively, the DOST is less competitive than the PST in localized image texture analysis. The PST features may help with texture-based lesion classification in MS based on clinical brain MRI scans following further verification.

RESEARCH ON 3D BUILDING VISUALIZATION BASED ON TEXTURE SIMPLIFICATION AND FRACTAL COMPRESSION

Abstract. There are some problems in the visualization of 3D model of buildings at present, such as redundant texture data and large memory occupation of loading texture data, which pose challenges to the smooth loading of 3D building visualization. In this paper, we propose a method which using fractal quadtrees to simplify texture data and compress management. Firstly, this method uses the fractal self-similarity and the feature that the same texture has the same fractal dimension to screen all the textures, when the fractal dimension of multiple textures is within the threshold range. Radon transform will be performed on these textures to calculate their standard deviation and further simplify the texture. Secondly, we use fractal texture compression to create a multi-resolution texture data structure with quadtree structure, so that the building 3D model can achieve dynamic visualization of different scales. Finally, the parallel texture mapping of all faces of the 3D model of the building is implemented according to the texture calling rules. Experimental results demonstrate that the texture is managed by the method proposed in this paper, which shortens the loading time of texture and reduces the memory occupation by about 48.92%. Therefore, the method of texture simplification and compression proposed in this paper has a significant effect in the process of building 3D model visualization.

Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion

This work proposes a novel framework for brain tumor segmentation prediction in longitudinal multimodal MRI scans, comprising two methods; feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with tumor cell density feature to obtain tumor segmentation predictions in follow-up timepoints using data from baseline pre-operative timepoint. The cell density feature is obtained by solving the 3D reaction-diffusion equation for biophysical tumor growth modelling using the Lattice-Boltzmann method. The second method utilizes JLF to combine segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest (RF)-based tumor segmentation method; and (ii) another state-of-the-art tumor growth and segmentation method, known as boosted Glioma Image Segmentation and Registration (GLISTRboost, or GB). We quantitatively evaluate both proposed methods using the Dice Similarity Coefficient (DSC) in longitudinal scans of 9 patients from the public BraTS 2015 multi-institutional dataset. The evaluation results for the feature-based fusion method show improved tumor segmentation prediction for the whole tumor(DSCWT = 0.314, p = 0.1502), tumor core (DSCTC = 0.332, p = 0.0002), and enhancing tumor (DSCET = 0.448, p = 0.0002) regions. The feature-based fusion shows some improvement on tumor prediction of longitudinal brain tumor tracking, whereas the JLF offers statistically significant improvement on the actual segmentation of WT and ET (DSCWT = 0.85 ± 0.055, DSCET = 0.837 ± 0.074), and also improves the results of GB. The novelty of this work is two-fold: (a) exploit tumor cell density as a feature to predict brain tumor segmentation, using a stochastic multi-resolution RF-based method, and (b) improve the performance of another successful tumor segmentation method, GB, by fusing with the RF-based segmentation labels.

Inpainting of Wide-Baseline Multiple Viewpoint Video.

We describe a non-parametric algorithm for multiple-viewpoint video inpainting. Uniquely, our algorithm addresses the domain of wide baseline multiple-viewpoint video (MVV) with no temporal look-ahead in near real time speed. A Dictionary of Patches (DoP) is built using multi-resolution texture patches reprojected from geometric proxies available in the alternate views. We dynamically update the DoP over time, and a Markov Random Field optimisation over depth and appearance is used to resolve and align a selection of multiple candidates for a given patch, this ensures the inpainting of large regions in a plausible manner conserving both spatial and temporal coherence. We demonstrate the removal of large objects (e.g., people) on challenging indoor and outdoor MVV exhibiting cluttered, dynamic backgrounds and moving cameras.

Real-time and multi-resolution rendering of the infrared characteristics for large-scale terrains

As a low-cost, efficient, digital simulation tool, real-time rendering of the infrared characteristics for large-scale terrains is typically interdisciplinary involving the rapid computation of heat transfer and infrared physics models, as well as tools associated with pattern recognition and computer graphics. This paper proposes an integrated real-time method that renders the infrared characteristics of large-scale terrains based on the classification of remote-sensing images in the visible band which can be obtained more conveniently. An energy balance model for terrains composed of different surface materials is established. This model is used in calculating the infrared characteristics of different terrain features and in generating a multi-resolution texture pyramid with the classification information. The infrared calculation results were verified by comparing with results from real world data. Out-of-core rendering method is used to load the mesh data and textures of the large-scale terrains. The process meets real-time demands when rendering terrain composed of over 200 GB mesh data and 15 LOD (level of detail).

A computerised framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors

A computerised framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors

A computerised framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors

The present work proposes a computerised framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors. For this study, 480 MLO view digitised screen film mammograms have been taken from the DDSM dataset. A fixed ROIs size of 128 × 128 pixels are cropped from the centre location of each mammographic image. Three texture features are computed in multi-resolution transform domain, where each ROI is decomposed up to 2nd level using ten different compact support wavelet filters resulting 16 sub-band feature images. Two step feature optimisation approach (feature pruning followed by feature space dimensionality reduction using PCA) is applied. In feature pruning stage, the TFV corresponding to best basis feature is selected; result of feature pruning stage is PTFV. This PTFV is subjected to PCA for feature space dimensionality reductions. After the application, PCA accuracy increases from 92.1% to 97.9%.

Quantitative MR Image Analysis for Brian Tumor.

This paper presents an integrated quantitative MR image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation respectively. We first obtain mathematical algorithm to compute a novel Generalized multifractional Brownian motion (GmBm) texture feature. We then demonstrate efficacy of multiple multiresolution texture features including regular fractal dimension (FD) texture, and stochastic texture such as multifractional Brownian motion (mBm) and GmBm features for robust tumor and other abnormal tissue segmentation in brain MRI. We evaluate these texture and associated intensity features to effectively delineate multiple abnormal tissues within and around the tumor core, and stroke lesions using large scale public and private datasets.

Training-Based Gradient LBP Feature Models for Multiresolution Texture Classification.

Local binary pattern (LBP) is a simple, yet efficient coding model for extracting texture features. To improve texture classification, this paper designs a median sampling regulation, defines a group of gradient LBP (gLBP) descriptors, proposes a training-based feature model mapping method, and then develops a texture classification frame using the multiresolution feature fusion of four gLBP descriptors. Cooperated by median sampling, four descriptors encode a pixel respectively by central gradient, radial gradient, magnitude gradient and tangent gradient to generate initial gLBP patterns. The feature mapping models of gLBP descriptors are constructed by the maximal relative-variation rate (mr2) of rotation-invariant patterns, and then prestored as mapping lookup files. By mapping, initial patterns can be transformed into low-dimensional ones. And then it generates multiresolution texture features via the joint and concatenation of gLBP descriptors on different sampling parameters. A trained nearest neighbor classifier with chi-square distance is applied to classify textures by feature histograms. The experimental results of simulation on five public texture databases show that the proposed method is reliable and efficient in texture classification. In comparison with nine other similar approaches, including two state-of-the-art ones, the proposed method runs faster than most of them and also outperforms all of them in terms of classification accuracy and noise robustness. It achieves higher accuracy and has also better robustness to the Salt&Pepper and Gaussian noise added artificially into texture images.

Multi-resolution texture analysis for fingerprint based age-group estimation

In this paper the possibility of using digital fingerprints to estimate age-groups of human being, particularly children is investigated. To our knowledge, age-group estimation in humans, using digital fingerprints have not been addressed formally. Age-group estimation can be applied in many areas like on-line child protection, access control and customized internet services etc. Motivated by the fact that human digital fingerprint vary in texture as the person ages, a multi-resolution texture approach for automatic age-group estimation has been presented in this paper. Three standard classifiers were used to judge the accuracy of the proposed method. In the process of this research study, a novel method for digital fingerprint reference point generation was developed, which provides reference point for very poor quality images also. The proposed reference point generation method is compared with core-point method using FG-NET DB1 dataset. Experimental results proves that a digital fingerprint can be used to identify age-groups, particularly children. A classification accuracy of 80 percent was achieved for children below the age of 14 by using the aforesaid method.

Artificial vision system for particle size characterization from bulk materials

This study shows how to develop a fast, reliable, and non-invasive artificial vision system to quantitatively estimate the particle size distribution of granular products. The system, based on multivariate and multiresolution texture analysis, uses digital images of the bulk material to extract quantitative information on the particle size ranges appearing in each image and on their weight proportion independently of the shape of the particle distribution (mono- or multi-modal). The method is applied to a wet-granulated product (namely, microcrystalline cellulose), and it is shown that the size distributions can be estimated accurately. The system performance is discussed in the light of an application in the automated monitoring of particle size distribution in industrial processes.

A GPU-based statistical image up-sampling method by using edge templates

Image up-sampling is very important in different fields, so an appropriate high-quality fast and efficient image up-sampling method is needed. Many interpolation-based up-sampling methods have been proposed by many researchers, but the quality of the resulting images is not satisfactory. The details of these images often cannot be accepted when we use them in many fields. On one hand, some of these methods are very fast, but produce images that are lacking many details and information of the original image; the others can produce high quality images, but the methods are very slow. In this paper, we propose a fast statistical image up-sampling method, and we use GPU to accelerate our up-sampling algorithm. We can obtain high quality images based on reducing the input resolution-grids dependency artefacts. And we can rebuild low resolution images' sharp edges fast and get high-quality up-sampled images in real time. We have applied this method in the multi-resolution texture generation of large scale terrain rendering. Experiments prove that our method can achieve ideal effects in real time.

A GPU-based statistical image up-sampling method by using edge templates

Image up-sampling is very important in different fields, so an appropriate high-quality fast and efficient image up-sampling method is needed. Many interpolation-based up-sampling methods have been proposed by many researchers, but the quality of the resulting images is not satisfactory. The details of these images often cannot be accepted when we use them in many fields. On one hand, some of these methods are very fast, but produce images that are lacking many details and information of the original image; the others can produce high quality images, but the methods are very slow. In this paper, we propose a fast statistical image up-sampling method, and we use GPU to accelerate our up-sampling algorithm. We can obtain high quality images based on reducing the input resolution-grids dependency artefacts. And we can rebuild low resolution images' sharp edges fast and get high-quality up-sampled images in real time. We have applied this method in the multi-resolution texture generation of large scale terrain rendering. Experiments prove that our method can achieve ideal effects in real time.