Texture-based Algorithm Research Articles

A multiangle image aggregation analysis and association algorithm for GNSS-based InSAR

ABSTRACT In the global navigation satellite system-based synthetic aperture radar interferometry (GNSS-based InSAR) system, 3D deformation retrieval needs to associate multiangle images, but due to the low 2D resolution, traditional texture-based algorithm cannot be used in GNSS-based system. At the same time the association error introduced by the elevation error also cannot be compensated for. In this paper, a multiangle images aggregation analysis and association algorithm is proposed under elevation error conditions. Instead of using texture information, focus positions from different angles are adopted here. First, the focus position with elevation error is modelled in the bistatic configuration. Then, the images association is achieved based on the aggregation analysis, which is further quantitatively expressed through minimum circle principle. Raw data from four satellites are used to demonstrate the validity of the proposed algorithm.

Contour Sparse Representation Using Voting Based Matching Pursuit for Road Marking Recognition

The road marking recognition is an important part of road scene understanding based on computer vision. In road marking recognition, the contour-based algorithm such as generalized Hough transform (GHT) is more effective than the texture-based algorithm for no texture noise effect. However, there are a lot of redundant calculations in template matching which reduces the system efficiency. This paper presents a voting based matching pursuit (VMP) algorithm for locating reference points automatically in solving the sparse optimization problem, which achieves automatic alignment of samples in recognition. Then a dictionary learning method based on VMP algorithm is proposed, which uses a simple strategy to update the dictionary elements. The experimental results from two data sets have shown that the system efficiency is significant improved while ensuring the accuracy rate by the proposed method.

Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment.

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.

Abstract 4217: First pre-clinical validation of radiogenomics in glioblastoma

Abstract A plethora of Magnetic Resonance Imaging (MRI) features have been correlated to cancer genomics to date, however, none have established causality. Here, we present an in vivo xenograft RNA interference validated, potentially clinically applicable test method termed “Magnetic Resonance Radiomic Sequencing” (MRRS) for the noninvasive detection of cancer genomics in Glioblastoma. MRRS comprehensively assesses the entire tumor mass using imaging texture-based algorithms that generate thousands of variables (features) inherent to the tumor. Two independent glioblastoma stem cells (GSC1 and GSC3) harboring doxycycline inducible short hairpin RNA against Periostin (POSTN), a gene previously identified in our radiogenomic screen, were implanted at orthotopic location in nude mouse brain. In vivo knockdown of >90% and ∼40% POSTN gene was achieved in GSC3 and GSC1 respectively. The T2 and T1 post MRI texture features, in edema and contrast enhancement phenotype features were compared between doxycycline (POSTN knockdown) and sucrose (control) group of mice using T test statistics. The significant features were included in a Stepwise Forward Logistic Regression analysis to build the final predictive model. The accuracy of the model was tested using ROC cure analysis. Among 3600 features in GSC3 mice cohort, 117 features were significantly (p value<0.05) different between the two groups. The significant features were included in a Stepwise Forward Logistic Regression analysis, 2 textures features (feature 234 of edema T1 and feature 251 of edema T2) were selected to be included in the final predictive model. The AUC of the model with leave one out cross validation method was 100%. The similar analyses were done in the GSC1 mice. The final predictive model in the GSC1 group was statistically insignificant (p value = 0.15) with AUC (95% CI) = 73% (46%-98%), suggesting that MRRS is reflective of underlying gene expression levels. Our results therefore describe the ‘first mouse model derived MRRS signature to describe a causal link of gene alteration to MRRS. This novel test method may open an avenue for human-mouse matched co-clinical trials and noninvasive Radiogenomic diagnostics. Citation Format: Pascal Zinn, Sanjay Singh, Markus M. Luedi, Faramak Zandi, Aikaterini Kotrotsou, Masumeh Hatami, Ginu Thomas, Ahmed Elakkad, Joy Gumin, Erik P. Sulman, Frederick Lang, David Piwnica-Worms, Rivka R. Colen. First pre-clinical validation of radiogenomics in glioblastoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4217.

A comparative study of steganography designs based on multiple FPGA platforms

Steganography methods conceal covert messages inside communicated data. Field-programmable gate array FPGA hardware implementation provides speed, flexibility and configurability. It is extremely difficult to compare published results from different platforms and technologies. The goal of our research work is to mitigate the dependency by examining implementations from multiple FPGA platforms. The research studies the implementations of 12 spatial steganography methods using Altera and Xilinx FPGAs. The methods include mix-bit LSB, least significant bit LSB, random LSB and texture-based algorithms. The objective of the research is to develop platform-independent resources, timing, power and energy models; to empower future steganography research. Further, the article evaluates steganography methods using typical performance metrics as well as a novel performance metric. The results suggest that the mix-bit methods exhibit good performance across most of the metrics. However, when image quality is a concern, the two-bit LSB is the front runner.

Supervised image segmentation using Q-Shift Dual-Tree Complex Wavelet Transform coefficients with a texton approach

In this study, we propose a simple and efficient texture-based algorithm for image segmentation. This method constitutes computing textons and bag of words (BOWs) learned by support vector machine (SVM) classifiers. Textons are composed of local magnitude coefficients that arise from the Q-Shift Dual-Tree Complex Wavelet Transform (DT-CWT) combined with color components. In keeping with the needs of our research context, which addresses land cover mapping from remote images, we use a few small texture patches at the training stage, where other supervised methods usually train fully representative textures. We accounted for the scale and rotation invariance issue of the textons, and three different invariance transforms were evaluated on DT-CWT-based features. The largest contribution of this study is the comparison of three classification schemes in the segmentation algorithm. Specifically, we designed a new scheme that was especially competitive and that uses several classifiers, with each classifier adapted to a specific size of analysis window in texton quantification and trained on a reduced data set by random selection. This configuration allows quick SVM convergence and an easy parallelization of the SVM-bank while maintaining a high segmentation accuracy. We compare classification results with textons made using the well-known maximum response filters bank and speed up robust features features as references. We show that DT-CWT textons provide better distinguishing features in the entire set of configurations tested. Benchmarks of our different method configurations were made over two substantial textured mosaic sets, each composed of 100 grey or color mosaics made up of Brodatz or VisTex textures. Lastly, when applied to remote sensing images, our method yields good region segmentation compared to the ENVI commercial software, which demonstrates that the method could be used to generate land cover maps and is suitable for various purposes in image segmentation.

Terrain Classification From Body-Mounted Cameras During Human Locomotion.

This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways. Firstly, for key frame selection, when regions with homogeneous texture characteristics are updated, the frequency variations of the textured surface are analyzed and used to adaptively define filter coefficients. Secondly, it is incorporated in the parameter estimation process where probabilities of path consistency are employed to improve terrain-type estimation. When tested with multiple classes that directly affect mobility-a hard surface, a soft surface, and an unwalkable area-our proposed method outperforms existing methods by up to 16%, and also provides improved robustness.

Despeckling of Synthetic Aperture Radar Images Using Monte Carlo Texture Likelihood Sampling

Speckle noise is found in synthetic aperture radar (SAR) images and can affect visualization and analysis. A novel stochastic texture-based algorithm is proposed to suppress speckle noise while preserving the underlying structural and texture detail. Based on a sorted local texture model and a Fisher-Tippett logarithmic-space speckle distribution model, a Monte Carlo texture likelihood sampling strategy is proposed to estimate the true signal. The algorithm is compared to six other classic and state-of-the-art despeckling techniques. The comparison is performed both on synthetic noisy images added and on actual SAR images. Using peak signal-to-noise ratio, contrast-to-noise ratio, and structural similarity index as image quality metrics, the proposed algorithm shows strong despeckling performance when compared to existing despeckling algorithms.

Segmentation of Choroidal Boundary in Enhanced Depth Imaging OCTs Using a Multiresolution Texture Based Modeling in Graph Cuts

The introduction of enhanced depth imaging optical coherence tomography (EDI-OCT) has provided the advantage of in vivo cross-sectional imaging of the choroid, similar to the retina, with standard commercially available spectral domain (SD) OCT machines. A texture-based algorithm is introduced in this paper for fully automatic segmentation of choroidal images obtained from an EDI system of Heidelberg 3D OCT Spectralis. Dynamic programming is utilized to determine the location of the retinal pigment epithelium (RPE). Bruch's membrane (BM) (the blood-retina barrier which separates the RPE cells of the retina from the choroid) can be segmented by searching for the pixels with the biggest gradient value below the RPE. Furthermore, a novel method is proposed to segment the choroid-sclera interface (CSI), which employs the wavelet based features to construct a Gaussian mixture model (GMM). The model is then used in a graph cut for segmentation of the choroidal boundary. The proposed algorithm is tested on 100 EDI OCTs and is compared with manual segmentation. The results showed an unsigned error of 2.48 ± 0.32 pixels for BM extraction and 9.79 ± 3.29 pixels for choroid detection. It implies significant improvement of the proposed method over other approaches like k-means and graph cut methods.

Age Estimation and Face Verification Across Aging Using Landmarks

Age estimation and face verification across aging are important problems with a wide range of applications. It is well known that age and identity information are encoded in both texture and shape of the face. Building on recent advances in landmark extraction and statistical techniques for landmark-based shape analysis, we consider these problems using facial shapes. We show that by using well-defined shape spaces and their associated geometry, one can obtain significant performance improvements in both age estimation and face verification. Toward this end, we propose to model the facial shapes as points on a Grassmann manifold. Age estimation and face verification are then considered as regression and classification problems on this manifold. Algorithms for regression and classification are designed to take into account the geometry of the underlying space. The proposed method is flexible and can be used as a standalone age estimator or classifier, and we also present methods for fusion with texture-based algorithms.

A spectral and texture-based unsupervised segmentation algorithm for human settlements land-cover extraction

It is crucial to evaluate human settlements to maintain human-based social development. To accurately extract human settlements land-cover information, this article focuses on the unsupervised image segmentation in high-resolution, remotely sensed imagery. J-based segmentation (JSEG) algorithm can offer good segmentation results, but it is highly dependent on image merging thresholds. In this article, a combined, unsupervised image segmentation algorithm is proposed, in which an initial segmentation result is first produced by JSEG and then the image texture is extracted for later region merging. The experiments on the high-resolution imagery show that, when compared with the results obtained by the original JSEG algorithm and eCognition, the proposed algorithm improves the segmentation accuracy.

Automatic Texture-Based Analysis in Ultrasound Imaging of Ovarian Masses

To assess the diagnostic accuracy of a new automatic texture-based algorithm (ATBA) in ultrasound imaging of ovarian masses and to compare its performance to subjective assessment by examiners with different levels of ultrasound experience. A total of 105 ultrasound images from three different groups of ovarian lesions (malignancies, functional cysts, and dermoid cysts) were evaluated using ATBA and by a total of 36 examiners with four different levels of experience (9 junior trainees, 8 senior trainees, 11 senior gynecologists, and 8 experts). Cohen's κ, Youden's indices, and the sensitivity and specificity of ATBA and of each observer were calculated for every subgroup of ovarian lesions. ATBA classified 78 of the 105 masses correctly (κ = 0.62) - results that were significantly better than those of the junior and senior trainees (p = 0.02 and p < 0.01), while differences from the group of level II examiners did not reach statistical significance (p = 0.27). The best diagnostic performance (κ = 0.70) was obtained by the group of expert level III ultrasonographers. The best classification rates overall, including both ATBA and subjective assessments, were achieved in the detection of functional cysts (Youden's indices from 0.73 to 0.85), while the poorest diagnostic performance was obtained for the classification of dermoid cysts (Youden's indices from 0.28 to 0.55). ATBA showed a significantly better diagnostic performance than observers with low or medium levels of experience, emphasizing its potential value for training purposes and in providing additional diagnostic assistance for inexperienced observers.

Quantifying the building stock from optical high-resolution satellite imagery for assessing disaster risk

This study uses high-resolution (HR) satellite imagery to quantify the stock of buildings, referred herein as building stock. The risk assessment requires information on the natural hazards and on the element at risk, that is the building stock in this article. This study combines (1) texture-based image processing to map built-up areas, (2) statistical sampling that allows locating the building samples and (3) photo-interpretation to encoding building footprints. Statistical inference is then used to quantify the building stock per class of building size. Legaspi in the Philippines is used as a case study. The results show that texture-based computer algorithms provide accurate area estimations of the built-up, that the detail of HR imagery allows the mapping of single buildings using photo-interpretation, and that a systematic sampling approach that uses building encoding and built-up maps can be used to quantify the building stock.

Applications of Texture-Based Flow Visualization

Flow visualization is a classic sub-field of scientific visualization. The task of flow visualization algorithms is to depict vector data, i.e., data with magnitude and direction. An important category of flow visualization techniques makes use of textures in order to convey the properties of a vector field. Recently, several research advances have been made in this special category, of dense, texture-based techniques. We present the application of the most recent texture-based techniques to real world data from oceanography and meteorology, computational fluid dynamics (CFD) in the automotive industry, and medicine. We describe the motivations for using texture-based algorithms as well as the important recent advancements required for their successful incorporation into industry grade software. Our goal is to appeal to practitioners in the field interested in learning how these recent techniques can help them gain insight into problems that engineers and other professionals may be faced with on a daily basis. Many of these applications have only recently become possible.

Motion-Based Template Matching for Obstacle Detection

A matching algorithm that evaluates the difference between model and calculated flows for obstacle detection in video sequences is presented. A stabilization method for obstacle detection by median filtering to overcome instability in the computation of optical flow is also presented. Since optical flow is a scene-independent measurement, the proposed algorithm can be applied to various situations, whereas most of existing color- and texture-based algorithms depend on specific scenes, such as roadway and indoor scenes. An experiment is conducted with three real image sequences, in which a static box or a moving toy car appears, to evaluate the performance in terms of accuracy under varying thresholds using a receiver operating characteristic (ROC) curve. For the three image sequences, the ROC curves show, in the best case, that the false positive fraction and the true positive fraction is 19.0% and 79.6%, 11.4% and 84.5%, 19.0% and 85.4%, respectively. The processing time per frame is 19.38msec. on 2.0GHz Pentium 4, which is less than the video-frame rate.

Interactive visualization of three-dimensional vector fields with flexible appearance control

In this paper, we present an interactive texture-based algorithm for visualizing three-dimensional steady and unsteady vector fields. The goal of the algorithm is to provide a general volume rendering framework allowing the user to compute three-dimensional flow textures interactively and to modify the appearance of the visualization on the fly. To achieve our goal, we decouple the visualization pipeline into two disjoint stages. First, flow lines are generated from the 3D vector data. Various geometric properties of the flow paths are extracted and converted into a volumetric form using a hardware-assisted slice sweeping algorithm. In the second phase of the algorithm, the attributes stored in the volume are used as texture coordinates to look up an appearance texture to generate both informative and aesthetic representations of the vector field. Our algorithm allows the user to interactively navigate through different regions of interest in the underlying field and experiment with various appearance textures. With our algorithm, visualizations with enhanced structural perception using various visual cues can be rendered in real time. A myriad of existing geometry-based and texture-based visualization techniques can also be emulated.

Detection of pigment network in dermatoscopy images using texture analysis

Dermatoscopy, also known as dermoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such prominent feature is the pigment network. Two texture-based algorithms are developed for the detection of pigment network. These methods are applicable to various texture patterns in dermatoscopy images, including patterns that lack fine lines such as cobblestone, follicular, or thickened network patterns. Two texture algorithms, Laws energy masks and the neighborhood gray-level dependence matrix (NGLDM) large number emphasis, were optimized on a set of 155 dermatoscopy images and compared. Results suggest superiority of Laws energy masks for pigment network detection in dermatoscopy images. For both methods, a texel width of 10 pixels or approximately 0.22 mm is found for dermatoscopy images.

Infrared target detection with probability density functions of wavelet transform subbands.

We report the development of a wavelet multiresolution texture-based algorithm that uses the probability density functions (PDFs) of the subband of the wavelet decomposition of an image. The moments of these pdfs are used in a clustering algorithm to segment the targets from their background clutter. Using the tools of experimental methodology, we evaluate the performance of this algorithm on real infrared imagery under varying algorithm parameter sets as well as scene, image, and false-alarm conditions. We estimate a set of multidimensional predictive analytic performance models that relate the detection probabilities as functions of false alarm, algorithm internal parameter, target pixel number, target-to-background interference ratio, target-interference ratio, and Fechner-Weber and local entropy metrics in the scene. These models can be used to predict performance in regions were no data are available and to optimize performance by selection of the optimum parameter and constant false-alarm values in regions with known scene and metric conditions.

Text analysis using local energy

This paper presents a biologically inspired texture-based algorithm using local energy analysis for (1) segmenting text embedded in clutter and (2) classifying text scripts without any explicit knowledge of the type of text present. The local energy model has been shown to work well in texture analysis, where texture segmentation and discrimination are preattentive tasks in human vision. The algorithm for text segmentation involves computing the local energy using a bank of orthogonal pairs of Gabor filters at various orientation and frequency. Similarly, local energy analysis is applied to the task of text script classification using a set of descriptors derived from the local energy information. The segmentation algorithm is quite successful in segmenting text of any arbitrary language script from real images with or without noise. It is also invariant to scale, rotation and position changes in text. The classification scheme was tested on 16 languages with the results obtained consistent with visual classification performed by humans. The scheme is insensitive to orientation of the text script.

Detection of solid pigment in dermatoscopy images using texture analysis.

BACKGROUND/AIMS: Epiluminescence microscopy (ELM), also known as dermoscopy or dermatoscopy, is a non-invasive, in vivo technique, that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such feature is the solid pigment, also called the blotchy pigment or dark structureless area. Our goal was to automatically detect this feature and determine whether its presence is useful in distinguishing benign from malignant pigmented lesions. METHODS: Here, a texture-based algorithm is developed for the detection of solid pigment. The factors d and a used in calculating neighboring gray level dependence matrix (NGLDM) numbers were chosen as optimum by experimentation. The algorithms are tested on a set of 37 images. A new index is presented for separation of benign and malignant lesions, based on the presence of solid pigment in the periphery. RESULTS: The NGLDM large number emphasis N2 was satisfactory for the detection of the solid pigment. Nine lesions had solid pigment detected, and among our 37 lesions, no melanoma lacked solid pigment. The index for separation of benign and malignant lesions was applied to the nine lesions. We were able to separate the benign lesions with solid pigment from the malignant lesions with the exception of only one lesion, a Spitz nevus that mimicked a malignant melanoma. CONCLUSION: Texture methods may be useful in detecting important dermatoscopy features in digitized images and a new index may be useful in separating benign from malignant lesions. Testing on a larger set of lesions is needed before further conclusions can be made.