Curvilinear Structures Research Articles

Rock Fracture Centerline Extraction based on Hessian Matrix and Steger Algorithm

The rock fracture detection by image analysis is significant for fracture measurement and assessment engineering. The paper proposes a novel image segmentation algorithm for the centerline tracing of a rock fracture based on Hessian Matrix at Multi-scales and Steger algorithm. A traditional fracture detection method, which does edge detection first, then makes image binarization, and finally performs noise removal and fracture gap linking, is difficult for images of rough rock surfaces. To overcome the problem, the new algorithm extracts the centerlines directly from a gray level image. It includes three steps: (1) Hessian Matrix and Frangi filter are adopted to enhance the curvilinear structures, then after image binarization, the spurious-fractures and noise are removed by synthesizing the area, circularity and rectangularity; (2) On the binary image, Steger algorithm is used to detect fracture centerline points, then the centerline points or segments are linked according to the gap distance and the angle differences; and (3) Based on the above centerline detection roughly, the centerline points are searched in the original image in a local window along the direction perpendicular to the normal of the centerline, then these points are linked. A number of rock fracture images have been tested, and the testing results show that compared to other traditional algorithms, the proposed algorithm can extract rock fracture centerlines accurately.

Mechanically Self-Assembled, Three-Dimensional Graphene-Gold Hybrid Nanostructures for Advanced Nanoplasmonic Sensors.

Hybrid structures of graphene and metal nanoparticles (NPs) have been actively investigated as higher quality surface enhanced Raman spectroscopy (SERS) substrates. Compared with SERS substrates, which only contain metal NPs, the additional graphene layer provides structural, chemical, and optical advantages. However, the two-dimensional (2D) nature of graphene limits the fabrication of the hybrid structure of graphene and NPs to 2D. Introducing three-dimensionality to the hybrid structure would allow higher detection sensitivity of target analytes by utilizing the three-dimensional (3D) focal volume. Here, we report a mechanical self-assembly strategy to enable a new class of 3D crumpled graphene-gold (Au) NPs hybrid nanoplasmonic structures for SERS applications. We achieve a 3D crumpled graphene-Au NPs hybrid structure by the delamination and buckling of graphene on a thermally activated, shrinking polymer substrate. We also show the precise control and optimization of the size and spacing of integrated Au NPs on crumpled graphene and demonstrate the optimized NPs' size and spacing for higher SERS enhancement. The 3D crumpled graphene-Au NPs exhibits at least 1 order of magnitude higher SERS detection sensitivity than that of conventional, flat graphene-Au NPs. The hybrid structure is further adapted to arbitrary curvilinear structures for advanced, in situ, nonconventional, nanoplasmonic sensing applications. We believe that our approach shows a promising material platform for universally adaptable SERS substrate with high sensitivity.

Effective properties of regular elastic laminated shell composite

The manuscript offers a methodology to solve an analytical model of a heterogeneous elastic problem for curvilinear layered structures, using the two scales asymptotic homogenization method (AHM). The local problems and the mechanical properties of the local functions were derived. The analytical modeling for the linear elastic problem considering quasi-periodic multi-layered curvilinear composites and the corresponding homogenized problem were obtained. The analytic expression of the effective stress for curvilinear composites is presented. In order to validate the presented model, comparisons with a computational modeling and experimental results for Fibonacci laminated composite and wavy laminated structure are given. The methodology is applied to composites with thickness variation where the effective coefficients were computed and a comparison between the results reported by AHM and numerical analysis given by finite element method (FEM) is presented. Finally, the aorta is studied as a curvilinear laminated shell composite and the above results were used to determinate the effective elastic tensor for healthy and unhealthy aorta using AHM and FEM.

Automated and optimal detection of 3D articular cartilage using undecimated wavelets in MRI

Articular cartilage is a thin and curvilinear structure whose image may get corrupted with noise during acquisition process. Image segmentation of this structure depends on expertise of an operator. In this paper, we propose an automated cartilage detection technique using 3D histogram and wavelet multiscale singularity analysis. 3D undecimated wavelet transform is implemented on MRI volume to obtain wavelet coefficients, which are used to determine an adaptive threshold for a given local resolution and a global threshold value. Local threshold helps to segment foreground cartilage edge details and is obtained using maximum likelihood estimate of the 3D histogram for a selected confidence level of intensity histogram. A global threshold is used to optimize coefficients using wavelet multiresolution singularity. The final wavelet coefficients are used to obtain a 3D model of cartilage tissue. The proposed method has been tested and validated using MRI and phantom datasets of articular cartilage. Quantitative analysis has been performed using mean square error (MSE), signal-to-noise ratio (SNR) and volumetric estimation of the datasets for different confidence and noise levels. The proposed method displays reduction in MSE for both denoised and noisy MRI volumes at different standard deviations of noise with overall improvement in SNR.

Disparity Estimation on Stereo Mammograms.

We consider the problem of depth estimation on digital stereo mammograms. Being able to elucidate 3D information from stereo mammograms is an important precursor to conducting 3D digital analysis of data from this promising new modality. The problem is generally much harder than the classic stereo matching problem on visible light images of the natural world, since nearly all of the 3D structural information of interest exists as complex network of multilayered, heavily occluded curvilinear structures. Toward addressing this difficult problem, we formulate a new stereo model that minimizes a global energy functional to densely estimate disparity on stereo mammogram images, by introducing a new singularity index as a constraint to obtain better estimates of disparity along critical curvilinear structures. Curvilinear structures, such as vasculature and spicules, are particularly salient structures in the breast, and being able to accurately position them in 3D is a valuable goal. Experiments on synthetic images with known ground truth and on real stereo mammograms highlight the advantages of the proposed stereo model over the canonical stereo model.

Automatic Channel Network Extraction From Remotely Sensed Images by Singularity Analysis

Quantitative analysis of channel networks plays an important role in river studies. To provide a quantitative representation of channel networks, we propose a new method that extracts channels from remotely sensed images and estimates their widths. Our fully automated method is based on a recently proposed Multiscale Singularity Index that responds strongly to curvilinear structures but weakly to edges. The algorithm produces a channel map, using a single image where water and non-water pixels have contrast, such as a Landsat near-infrared band image or a water index defined on multiple bands. The proposed method provides a robust alternative to the procedures that are used in remote sensing of fluvial geomorphology and makes classification and analysis of channel networks easier. The source code of the algorithm is available at: http://live.ece.utexas.edu/research/cne/.

Detecting Curvilinear Features Using Structure Tensors.

Few published articles on curvilinear structures exist compared with works on detecting lines or corners with high accuracy. In medical ultrasound imaging, the structures that need to be detected appear as a collection of microstructures correlated along a path. In this paper, we investigated techniques that extract meaningful low-level information for curvilinear structures, using techniques based on structure tensor. We proposed a novel structure tensor enhancement inspired by bilateral filtering. We compared the proposed approach with five state-of-the-art curvilinear structure detectors. We tested the algorithms against simulated images with known ground truth and real images from three different domains (medical ultrasound, scanning electron microscope, and astronomy). For the real images, we employed experts to delineate the ground truth for each domain. Techniques borrowed from machine learning robustly assessed the performance of the methods (area under curve and cross validation). As a practical application, we used the proposed method to label a set of 5000 ultrasound images. We conclude that the proposed tensor-based approach outperforms the state-of-the-art methods in providing magnitude and orientation information for curvilinear structures. The evaluation methodology ensures that the employed feature-detection method will yield reproducible performance on new, unseen images. We published all the implemented methods as open-source software.

Correction method for line extraction in vision measurement.

Over-exposure and perspective distortion are two of the main factors underlying inaccurate feature extraction. First, based on Steger’s method, we propose a method for correcting curvilinear structures (lines) extracted from over-exposed images. A new line model based on the Gaussian line profile is developed, and its description in the scale space is provided. The line position is analytically determined by the zero crossing of its first-order derivative, and the bias due to convolution with the normal Gaussian kernel function is eliminated on the basis of the related description. The model considers over-exposure features and is capable of detecting the line position in an over-exposed image. Simulations and experiments show that the proposed method is not significantly affected by the exposure level and is suitable for correcting lines extracted from an over-exposed image. In our experiments, the corrected result is found to be more precise than the uncorrected result by around 45.5%. Second, we analyze perspective distortion, which is inevitable during line extraction owing to the projective camera model. The perspective distortion can be rectified on the basis of the bias introduced as a function of related parameters. The properties of the proposed model and its application to vision measurement are discussed. In practice, the proposed model can be adopted to correct line extraction according to specific requirements by employing suitable parameters.

PCA-derived respiratory motion surrogates from X-ray angiograms for percutaneous coronary interventions.

PurposeIntraoperative coronary motion modeling with motion surrogates enables prospective motion prediction in X-ray angiograms (XA) for percutaneous coronary interventions. The motion of coronary arteries is mainly affected by patients breathing and heartbeat. Purpose of our work is therefore to extract coronary motion surrogates that are related to respiratory and cardiac motion. In particular, we focus on respiratory motion surrogates extraction in this paper.MethodsWe propose a fast automatic method for extracting patient-specific respiratory motion surrogate from cardiac XA. The method starts with an image preprocessing step to remove all tubular and curvilinear structures from XA images, such as vessels and guiding catheters, followed by principal component analysis on pixel intensities. The respiratory motion surrogate of an XA image is then obtained by projecting its vessel-removed image onto the first principal component.ResultsThis breathing motion surrogate was demonstrated to get high correlation with ground truth diaphragm motion (correlation coefficient over 0.9 on average). In comparison with other related methods, the method we developed did not show significant difference (p>0.05), but did improve robustness and run faster on monoplane and biplane data in retrospective and prospective scenarios.Conclusionswe developed and evaluated a method in extraction of respiratory motion surrogate from interventional X-ray images that is easy to implement and runs in real time and thus allows extracting respiratory motion surrogates during interventions.

Fabrication of elastomeric scaffolds with curvilinear fibrous structures for heart valve leaflet engineering.

Native semi-lunar heart valves are composed of a dense fibrous network that generally follows a curvilinear path along the width of the leaflet. Recent models of engineered valve leaflets have predicted that such curvilinear fiber orientations would homogenize the strain field and reduce stress concentrations at the commissure. In the present work, a method was developed to reproduce this curvilinear fiber alignment in electrospun scaffolds by varying the geometry of the collecting mandrel. Elastomeric poly(ester urethane)urea was electrospun onto rotating conical mandrels of varying angles to produce fibrous scaffolds where the angle of fiber alignment varied linearly over scaffold length. By matching the radius of the conical mandrel to the radius of curvature for the native pulmonary valve, the electrospun constructs exhibited a curvilinear fiber structure similar to the native leaflet. Moreover, the constructs had local mechanical properties comparable to conventional scaffolds and native heart valves. In agreement with prior modeling results, it was found under quasi-static loading that curvilinear fiber microstructures reduced strain concentrations compared to scaffolds generated on a conventional cylindrical mandrels. Thus, this simple technique offers an attractive means for fabricating scaffolds where key microstructural features of the native leaflet are imitated for heart valve tissue engineering.

Non-Rigid Graph Registration Using Active Testing Search.

We present a new approach for matching sets of branching curvilinear structures that form graphs embedded in R2 or R3 and may be subject to deformations. Unlike earlier methods, ours does not rely on local appearance similarity nor does require a good initial alignment. Furthermore, it can cope with non-linear deformations, topological differences, and partial graphs. To handle arbitrary non-linear deformations, we use Gaussian process regressions to represent the geometrical mapping relating the two graphs. In the absence of appearance information, we iteratively establish correspondences between points, update the mapping accordingly, and use it to estimate where to find the most likely correspondences that will be used in the next step. To make the computation tractable for large graphs, the set of new potential matches considered at each iteration is not selected at random as with many RANSAC-based algorithms. Instead, we introduce a so-called Active Testing Search strategy that performs a priority search to favor the most likely matches and speed-up the process. We demonstrate the effectiveness of our approach first on synthetic cases and then on angiography data, retinal fundus images, and microscopy image stacks acquired at very different resolutions.

Hypopituitarism with Moyamoya Disease Involving the Posterior Pituitary

ABSTRACT Objective: To describe a case of moyamoya disease associated with hypopituitarism. Methods: We present a case of moyamoya disease presenting with hypogonadotrophic hypogonadism and central diabetes insipidus and review the literature. Results: A 22-year-old female presented with secondary amenorrhea with loss of secondary sexual characteristics. Her follicle-stimulating hormone and luteinizing hormone levels were 1.4 mIU/mL and <0.1 mIU/mL, respectively. The results of water deprivation test indicated central diabetes insipidus. Her thyroid-stimulating hormone, thyroxine, and post-Synacthen cortisol levels were normal, and her prolactin was measured as 52.2 ng/mL (normal <20 ng/mL). Contrast-enhanced magnetic resonance imaging showed a partial empty sella with enhanced linear and curvilinear structures in the suprasellar cistern. Magnetic resonance angiography showed narrowing of the apices of the bilateral intracranial carotid, middle cerebral, and anterior cerebral arteries. It also showed coll...

Learning Separable Filters.

Learning filters to produce sparse image representations in terms of over-complete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, making their use computationally expensive. In this paper, we show that such filters can be computed as linear combinations of a smaller number of separable ones, thus greatly reducing the computational complexity at no cost in terms of performance. This makes filter learning approaches practical even for large images or 3D volumes, and we show that we significantly outperform state-of-the-art methods on the curvilinear structure extraction task, in terms of both accuracy and speed. Moreover, our approach is general and can be used on generic convolutional filter banks to reduce the complexity of the feature extraction step.

Automatic cluster selection using gap statistics for pattern-based multi-point geostatistical simulation

An automatic cluster number selection algorithm is proposed for multi-point geostatistical simulation. The multi-point simulation is performed by extracting patterns from training image. The computational time of the pattern-based simulation is significantly reduced by dimension reduction of patterns by principal component analysis (PCA). The traditional PCA is used for its simplicity and computational ease. The patterns are classified using their principal components (PCs) by the k-means clustering algorithm. The number of clusters is selected automatically by calculating the gap statistics. The conditional cumulative density function (ccdf) for each class was generated based on the frequency of the central node value of the template. For sequential simulation, the similarity of the conditioning data with the class prototypes is measured using the L2-norm. The ccdf of best-matched class is used to draw a pattern from a class. The algorithm is validated with examples of conditional and unconditional simulation. The results show that the spatial continuity in terms of reproduction of curvilinear structure is well reproduced in all examples. The reproductions of first- and second-order statistics are also very good for all examples. A comparative study with the wavesim and filtersim techniques show that the proposed algorithm performed better than the filtersim and performed more or less very similar to the wavesim algorithm; however, the computational time of the proposed method is similar to filtersim and relatively less than that of the wavesim algorithm. The sensitivity of the algorithm on a number of PCs and the number of clusters have also been tested. Results revealed that automatic cluster selection helps to improve the performance of the proposed method.

Stochastic extraction of elongated curvilinear structures with applications.

The automatic extraction of elongated curvilinear structures (CLSs) is an important task in various image processing applications, including numerous remote sensing, and biometrical and medical problems. To address this task, we develop a stochastic approach that relies on a fixed-grid, localized Radon transform for line segment extraction and a conditional random field model to incorporate local interactions and refine the extracted CLSs. We propose several different energy data terms, the appropriate choice of which allows us to process images with different noise and geometry properties. The contribution of this paper is the design of a flexible and robust elongated CLS extraction framework that is comparatively fast due to the use of a fixed-grid configuration and fast deterministic Radon-based line detector. We present several different applications of the developed approach, namely: 1) CLS extraction in mammographic images; 2) road networks extraction from optical remotely sensed images; and 3) line extraction from palmprint images. The experimental results demonstrate that the method is fairly robust to CLS curvature and can accurately extract blurred and low-contrast elongated CLS.

Quantitative morphological analysis of curvilinear network for microscopic image based on individual fibre segmentation (IFS).

Microscopic images of curvilinear fibre network structure like cytoskeleton are traditionally analysed by qualitative observation, which can hardly provide quantitative information of their morphological properties. However, such information is crucially contributive to the understanding of important biological events, even helps to learn about the inner relations hard to perceive. Individual fibre segmentation-based curvilinear structure detector proposed in this study can identify each individual fibre in the network, as well as connections between different fibres. Quantitative information of each individual fibre, including length, orientation and position, can be extracted; so are the connecting modes in the fibre network, such as bifurcation, intersection and overlap. Distribution of fibres with different morphological properties is also presented. No manual intervening or subjective judging is required in the analysing process. Both synthesized and experimental microscopic images have verified that the detector is capable to segment curvilinear network at the subcellular level with strong noise immunity. The proposed detector is finally applied to the morphological study on cytoskeleton. It is believed that the individual fibre segmentation-based curvilinear structure detector can greatly enhance our understanding of those biological images generated from tons of biological experiments.

Ridge-based method for finding curvilinear structures from noisy data

Extraction of curvilinear structures from noisy data is an essential task in many application fields such as data analysis, pattern recognition and machine vision. The proposed approach assumes a random process in which the samples are obtained from a generative model. The model specifies a set of generating functions describing curvilinear structures as well as sampling noise and background clutter. It is shown that ridge curves of the marginal density induced by the model can be used to estimate the generating functions. Given a Gaussian kernel density estimate for the marginal density, ridge curves of the density estimate are parametrized as the solution to a differential equation. Finally, a predictor–corrector algorithm for tracing the ridge curve set of such a density estimate is developed. Efficiency and robustness of the algorithm are demonstrated by numerical experiments on synthetic datasets as well as observational datasets from seismology and cosmology.

Inpainting images with curvilinear structures propagation

Inpainting images with smooth curvilinear structures interrupted is a challenging problem, because the structures are salient features sensitive to the human vision system and they are not easy to be completed in a visually pleasing way, especially when gaps are large. In this paper, we propose an approach to address this problem. A curve with a desired nice shape is first created to smoothly extend the missing structure from the known to unknown regions. As the curve partitions the unknown region into separate areas, textures can be filled independently into each area. We then adopt a patch-based texture inpainting method enhanced by a novel similarity measurement of patches. After that, very abrupt edges caused by different inpainted colours on their two sides need to be smoothed for natural colour transition across the curve. Experimental results demonstrate the effectiveness of the proposed approach.

Biologically derived soft conducting hydrogels using heparin-doped polymer networks.

The emergence of flexible and stretchable electronic components expands the range of applications of electronic devices. Flexible devices are ideally suited for electronic biointerfaces because of mechanically permissive structures that conform to curvilinear structures found in native tissue. Most electronic materials used in these applications exhibit elastic moduli on the order of 0.1–1 MPa. However, many electronically excitable tissues exhibit elasticities in the range of 1–10 kPa, several orders of magnitude smaller than existing components used in flexible devices. This work describes the use of biologically derived heparins as scaffold materials for fabricating networks with hybrid electronic/ionic conductivity and ultracompliant mechanical properties. Photo-cross-linkable heparin–methacrylate hydrogels serve as templates to control the microstructure and doping of in situ polymerized polyaniline structures. Macroscopic heparin-doped polyaniline hydrogel dual networks exhibit impedances as low as Z = 4.17 Ω at 1 kHz and storage moduli of G′ = 900 ± 100 Pa. The conductivity of heparin/polyaniline networks depends on the oxidation state and microstructure of secondary polyaniline networks. Furthermore, heparin/polyaniline networks support the attachment, proliferation, and differentiation of murine myoblasts without any surface treatments. Taken together, these results suggest that heparin/polyaniline hydrogel networks exhibit suitable physical properties as an electronically active biointerface material that can match the mechanical properties of soft tissues composed of excitable cells.

Gossypiboma

Sir: The retained surgical sponge, or gossypiboma, has become a rare event with the advent of standardized surgical counting. The Association of Operating Room Nurses requires that sponge counts be performed once at the start and twice at the conclusion of every operation involving an open cavity. In addition, the World Health Organization recommends only x-ray–detectable sponges be placed in body cavities and that non–x-ray–detectable sponges only be used after skin closure.1,2 When count discrepancies cannot be reconciled, a plain radiograph should be obtained. Although retained objects typically occur in operations involving the viscera, soft-tissue procedures are not immune from retained objects. We recently treated a 64-year-old woman who underwent bilateral breast augmentation with silicone implants in another country. She presented to our office 7 months later with a 4-cm area of firmness and associated discomfort in the upper outer quadrant of her right breast. On mammography, no abnormality was seen. An ultrasound revealed a 3-cm, hyperechoic, wavy curvilinear structure with dense posterior acoustic shadowing at the 10 o’clock position (Fig. 1). Given our suspicion of a retained foreign body, she was taken to the operating room, where a retained sponge was encountered that did not contain a radiopaque marker (Fig. 2).Fig. 1: Ultrasound scan of the right breast over the area of palpable abnormality in the upper outer quadrant shows a hyperechoic, wavy, curvilinear structure with dense posterior acoustic shadowing suggestive of a retained foreign body.Fig. 2: Intraoperative photograph shows retained gauze sponge in the patient’s right breast. There was no radiopaque marker present on the sponge.Clinical suspicion for gossypiboma may be raised by the presence of a palpable mass. In cases where reports exist for the patient’s prior operation, or the facility is known, the diagnosis can be made using plain radiographs alone. However, when the facility is unknown, one cannot be certain that x-ray–detectable sponges were used intraoperatively. In such cases, a negative plain radiograph is not sufficient to rule out gossypiboma and additional imaging may be required for diagnosis. On computed tomographic scans, a gossypiboma appears as a well-circumscribed mass with an enhancing capsule, generally 2 to 8 mm thick, containing internal heterogeneous densities with a wavy or striped appearance. On ultrasound, the gossypiboma appears as an echogenic center with a hypoechoic rim and a sharply delineated acoustic shadow from attenuation of the sound waves by the sponge fibers.3,4 The need for this more complex workup may become more frequent with the rise in medical tourism. An estimated 750,000 Americans sought medical care abroad in 2007, and 648,000 did so in 2008.5 These medical tourists seek care in a variety of countries, including less-developed nations, and a variety of contexts, including cosmetic surgery. Proponents argue that medical tourism offers decreased patient cost, expedited access to care, and increased choice. Unfortunately, there are few data on patient safety and clinical outcomes. The World Health Organization’s guidelines for the labeling and counting of sponges may be followed to varying degrees internationally, and as more patients travel abroad for cosmetic procedures but return to the United States for management of complications, the plastic surgeon must consider the possibility of retained objects. Induration, pain, or a palpable mass should prompt a diagnostic workup beginning with a radiograph. If it is unrevealing, a computed tomographic scan or ultrasound should follow, with a return to the operating room if the imaging findings suggest gossypiboma. DISCLOSURE The authors have no financial interest to declare in relation to the content of this article. There was no internal or external financial support for this study. Neelesh A. Kantak, M.D. Richard G. Reish, M.D. Harvard Plastic Surgery Combined Residency Program Sumner A. Slavin, M.D. Samuel J. Lin, M.D. Division of Plastic Surgery Beth Israel Deaconess Medical Center Boston, Mass.