Showing papers by "Yongtian Wang published in 2017"

Volumetric Generation of Optical Vortices with Metasurfaces

Abstract: Recent advances in metasurfaces, i.e., two-dimensional arrays of engineered nanoscale inclusions that are assembled onto a surface, have revolutionized the way to control electromagnetic waves with ultrathin, compact components. The generation of optical vortex beams, which carry orbital angular momentum, has emerged as a vital approach to applications ranging from high-capacity optical communication to parallel laser fabrication. However, the typically bulky elements used for the generation of optical vortices impose a fundamental limit toward on-chip integration with subwavelength footprints. Here, we investigate and experimentally demonstrate a three-dimensional volumetric optical vortices generation based on light–matter interaction with a high-efficiency dielectric metasurface. By employing the concepts of Dammann vortex gratings and spiral Dammann zone plates, a 3D optical vortex array with micrometer spatial separation is achieved from visible to near-infrared wavelengths. Importantly, we show that...

Intensity and Compactness Enabled Saliency Estimation for Leakage Detection in Diabetic and Malarial Retinopathy

Abstract: Leakage in retinal angiography currently is a key feature for confirming the activities of lesions in the management of a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. This paper proposes a new saliency-based method for the detection of leakage in fluorescein angiography. A superpixel approach is firstly employed to divide the image into meaningful patches (or superpixels) at different levels. Two saliency cues, intensity and compactness, are then proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. The two saliency maps over different cues are fused using a pixel-wise multiplication operator. Leaking regions are finally detected by thresholding the saliency map followed by a graph-cut segmentation. The proposed method has been validated using the only two publicly available datasets: one for malarial retinopathy and the other for diabetic retinopathy. The experimental results show that it outperforms one of the latest competitors and performs as well as a human expert for leakage detection and outperforms several state-of-the-art methods for saliency detection.

Renal Clearable Ag Nanodots for in Vivo Computer Tomography Imaging and Photothermal Therapy

Abstract: Albumin-stabilized Ag nanodots (ANDs) are prepared by a one-step biomineralization method. The highly crystallized nanodots have ultrasmall sizes (approximately 5.8 nm) and robust X-ray attenuation (5.7313 HU per mM Ag). The unlabeled ANDs are directly excreted from the body via the urine after in vivo X-ray computer tomography (CT) imaging application. ANDs could be used as CT imaging agents and effective photothermal therapy agents. Tumor growth inhibition reaches 90.2% after photothermal treatment with ANDs. ANDs are promising tools for in vivo CT imaging and clearable near-infrared-triggered theranostic agents.

Deep-Stacked Auto Encoder for Liver Segmentation

Abstract: Deep learning methods have been successfully applied to feature learning in medical applications. In this paper, we proposed a Deep Stacked Auto-Encoder (DSAE) for liver segmentation from CT images. The proposed method composes of three major steps. First, we learned the features with unlabeled data using the auto encoder. Second, these features are fine-tuned to classify the liver among other abdominal organs. Using this technique we got promising classification results on 2D CT data. This classification of the data helps to segment the liver from the abdomen. Finally, segmentation of a liver is refined by post processing method. We focused on the high accuracy of the classification task because of its effect on the accuracy of a better segmentation. We trained the deep stacked auto encoder (DSAE) on 2D CT images and experimentally shows that this method has high classification accuracy and can speed up the clinical task to segment the liver. The mean DICE coefficient is noted to be 90.1% which is better than the state of art methods.

Multi-region phase calibration of liquid crystal SLM for holographic display.

Abstract: The liquid crystal spatial light modulator (SLM) is able to provide flexible wavefront control, whereas the initial phase and its response distortions will heavily influence the modulation accuracy. The currently existing calibration methods are tedious and time consuming. A novel multi-region calibration method for minimizing those distortions is proposed. The entire panel is divided into several local regions based on the similarity of phase response characteristic. The nonlinear phase response and static phase distortion of each local region are calibrated in the iterative division procedure. The calibration method is theoretically analyzed and experimentally verified. For the Jasper 4 K SLM panel, when five local regions are built, the root mean error of linear phase shifts is reduced to 0.1 rad and the compensation accuracy of the static phase distortion reaches 0.24 wavelength. The calibrated SLM is applied for the color holographic display and the results show that the reconstructed image quality is improved significantly. The proposed method is simpler and faster because of the reasonable regional division and lower calibration complexity. It could be used for the calibration of various phase only or complex modulators with high space bandwidth product in the future.

Convex Hull Aided Registration Method (CHARM)

Abstract: Non-rigid registration finds many applications such as photogrammetry, motion tracking, model retrieval, and object recognition. In this paper we propose a novel convex hull aided registration method (CHARM) to match two point sets subject to a non-rigid transformation. First, two convex hulls are extracted from the source and target respectively. Then, all points of the point sets are projected onto the reference plane through each triangular facet of the hulls. From these projections, invariant features are extracted and matched optimally. The matched feature point pairs are mapped back onto the triangular facets of the convex hulls to remove outliers that are outside any relevant triangular facet. The rigid transformation from the source to the target is robustly estimated by the random sample consensus (RANSAC) scheme through minimizing the distance between the matched feature point pairs. Finally, these feature points are utilized as the control points to achieve non-rigid deformation in the form of thin-plate spline of the entire source point set towards the target one. The experimental results based on both synthetic and real data show that the proposed algorithm outperforms several state-of-the-art ones with respect to sampling, rotational angle, and data noise. In addition, the proposed CHARM algorithm also shows higher computational efficiency compared to these methods.

[POSTER] Prevention of Visually Induced Motion Sickness Based on Dynamic Real-Time Content-Aware Non-salient Area Blurring

Abstract: This paper proposes an innovative method for reducing the visually induced motion sickness (MS) occurred in a 3D immersive virtual environment (VE) by utilizing a flexible dynamic scene smoothing approach based on saliency analysis. A saliency model based on fully convolutional network (FCN) is first trained to establish the saliency map, then the probability maps representing the salient information and the non-salient information are combined to alter the field of view (FOV) by smoothing the non-salient area. An experiment is conducted to evaluate the performance of the proposed approach. The experimental data demonstrate that participants experiencing dynamic blurring VE reports a 50% reduction of the severity of MS symptom on average during the VR experience than the participants experiencing the control condition, which show that the proposed approach can be used to effectively prevent the visually induced MS in VR and support longer duration of users in VE.

Local incompressible registration for liver ablation surgery assessment.

Abstract: Purpose In liver microwave ablation (MWA) surgery, the ablation area covers the tumor to generate tissue necrosis and treat the cancer. As the liver deforms during the operation, deviation between the target area determined during pre-operative planning and the resultant ablation area is inevitable. Therefore, an accurate assessment of tumor coverage is crucial for treatment. Through registration between the pre- and post-operative livers, the ablation area is warped on the pre-operative liver for the computation of tumor coverage. However, large deformations between the pre- and post-operative livers are caused by multiple factors, and these diverse deformations make registration a challenging task. The purpose of this paper is to develop an automatic method that can accurately register post- to pre-operative livers. Methods In the proposed method, non-rigid deformations caused by respiratory movement and edema are separately considered and estimated by the local incompressible model in the registration of livers. The pre- and post-operative livers are first aligned by a rigid registration based on a convex hull. In the non-rigid registrations, local incompressible constraints are then set on the liver and the ablation area to estimate the deformations caused by respiratory movement and edema, respectively. The concatenation of the rigid and non-rigid deformations is used to warp the ablation area on the pre-operative liver. Results The proposed method was evaluated using clinical CT datasets from 20 patients. The Dice similarity coefficient (DSC) between the pre-operative and warped post-operative livers is 94.35%, the mean surface distance (MSD) between the livers is 1.65 mm, the mean hausdorff distance (HDD) between the livers is 3.36 mm, and the mean corresponding distance (MCD) between the corresponding landmarks is 1.70 mm. Compared with five other state-of-the-art methods, the proposed method achieves automatic ablation assessment with highly accurate registration. Conclusions The proposed method achieves a high accuracy for registering the livers. The sizes and positions of the ablation area and tumor are accurately compared for the assessment of ablation surgery. This article is protected by copyright. All rights reserved.

Global Patch Matching (GPM) for freehand 3D ultrasound reconstruction

Abstract: 3D ultrasound volume reconstruction from B-model ultrasound slices can provide more clearly and intuitive structure of tissue and lesion for the clinician. This paper proposes a novel Global Path Matching method for the 3D reconstruction of freehand ultrasound images. The proposed method composes of two main steps: bin-filling scheme and hole-filling strategy. For the bin-filling scheme, this study introduces two operators, including the median absolute deviation and the inter-quartile range absolute deviation, to calculate the invariant features of each voxel in the 3D ultrasound volume. And the best contribution range for each voxel is obtained by calculating the Euclidian distance between current voxel and the voxel with the minimum invariant features. Hence, the intensity of the filling vacant voxel can be obtained by weighted combination of the intensity distribution of pixels in the best contribution range. For the hole-filling strategy, three conditions, including the confidence term, the data term and the gradient term, are designed to calculate the weighting coefficient of the matching patch of the vacant voxel. While the matching patch is obtained by finding patches with the best similarity measure that defined by the three conditions in the whole 3D volume data. Compared with VNN, PNN, DW, FMM, BI and KR methods, the proposed Global Path Matching method can restore the 3D ultrasound volume with minimum difference. Experimental results on phantom and clinical data sets demonstrate the effectiveness and robustness of the proposed method for the reconstruction of ultrasound volume.

Real-Time Salient Object Detection Based on Fully Convolutional Networks

Abstract: Salient object detection allows to take into account the visual content of images. In this paper, we train a real-time saliency model based on fully convolutional network (FCN), and then combine the energy maps with Gaussian filters to generate a multi-resolution image. The proposed method has been tested both qualitatively and quantitatively, by considering a representative set of ground truth images labeled with corresponding salient objects. Experimental results demonstrate that the proposed deep model significantly is superior to the-state-of-the-art approaches.

Freeform optics for virtual and augmented reality

Abstract: Freeform optics, mixed with other new technologies, continue to push the frontier of head-worn displays for virtual reality and augmented reality, and deliver systems with high optical performance, better user experience, and low production cost.

Evaluation of labelling layout method for image-driven view management in augmented reality

Abstract: View management techniques are commonly used for the optimization of labelling layout of objects in augmented reality systems, in which penalty function is an effective method to get the optimal positions of labels. In this paper, an image-driven view management method to superimpose 2D labels to the objects under indoor environments such as sculpture and toy by minimizing penalty function is studied. A new penalty function is proposed to change the orientation of each leader line in the penalty elements and a modified search space method is integrated to improve the quality of labelling layout. Experiments are conducted to evaluate the labelling layout optimized by different penalty functions under different experiment conditions and the comparison of experimental results between different penalty functions shows that the proposed method represents the best results in terms of avoiding occlusion and efficiency to get the optimal layout.

A compactness based saliency approach for leakages detection in fluorescein angiogram

Abstract: This study has developed a novel saliency detection method based on compactness feature for detecting three common types of leakage in retinal fluorescein angiogram: large focal, punctate focal, and vessel segment leakage. Leakage from retinal vessels occurs in a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. The proposed framework consists of three major steps: saliency detection, saliency refinement and leakage detection. First, the Retinex theory is adapted to address the illumination inhomogeneity problem. Then two saliency cues, intensity and compactness, are proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. Finally, the leaking sites can be detected by masking the vessel and optic disc regions. The effectiveness of this framework has been evaluated by applying it to different types of leakage images with cerebral malaria. The sensitivity in detecting large focal, punctate focal and vessel segment leakage is 98.1, 88.2 and 82.7 %, respectively, when compared to a reference standard of manual annotations by expert human observers. The developed framework will become a new powerful tool for studying retinal conditions involving retinal leakage.

Method and system for navigating endoscopic surgery

Abstract: The invention provides a method and system for navigating endoscopic surgery The method comprises the steps of reading multi-modality medical image data; conducting image full affine matching using any one of the multi-modality medical image data as a reference image and using other medical image data as an image to be registered; conducting scene reconstruction and mixed rendering on image data after image full affine matching to obtain a virtual scene; using a fast registration method to complete the registration of CT navigation images and patient poses; conducting rapid calibration by means of convex hull optimized surface point cloud; tracking an endoscope and surgical tools, and obtaining the pose relationship between the endoscope and surgical tools and the body of a patient; obtaining a virtual scene view of the endoscope in the virtual scene according to the obtained pose relationship; conducting Gaussian function attenuation on the edge of the real-time image of the endoscope, and conducting blending with the virtual scene view of the endoscope to achieve scene layered rendering By the adoption of the method and system, the image rendering speed is increased, and navigation precision is improved

Evaluation of labelling layout methods in augmented reality

Abstract: View management techniques are commonly used for labelling of objects in augmented reality environments. Combining with image analysis, search space and adaptive representations, they can be utilized to achieve desired labelling tasks. However, the evaluation of different search space methods on labelling are still an open problem. In this paper, we propose an image analysis based view management method, which first adopts the image processing to superimpose 2D labels to the specific object. We then conduct three search space methods to an augmented reality scenario. Without the requirements of setting rules and constraints for occlusion among the labels, the results of three search space methods are evaluated by using objective analysis of related parameters. The evaluation results indicate that different search space methods could generate different time costs and occlusion, thereby affecting the final labelling effects.

[POSTER] An Accurate Calibration Method for Optical See-Through Head-Mounted Displays Based on Actual Eye-Observation Model

Abstract: Single point active alignment method (SPAAM) has become the basic calibration method for optical-see-through head-mounted displays since its appearance. However, SPAAM is based on a simple static pinhole camera model that assumes a static relationship between the user's eye and the HMD. Such theoretic defects lead to a limitation in calibration accuracy. We model the eye as a dynamic pinhole camera to account for the displacement of the eye during the calibration process. We use region-induced data enhancement (RIDE) to reduce the system error in the acquisition process. The experimental results prove that the proposed dynamic model performs better than the traditional static model, and the RIDE method can help users obtain a more accurate calibration result based on the dynamic model, which improves the accuracy significantly compared to the standard SPAAM.

Local Directional Probability Optimization for Quantification of Blurred Gray/White Matter Junction in Magnetic Resonance Image.

Abstract: The blurred gray/white matter junction is an important feature of focal cortical dysplasia (FCD) lesions. FCD is the main cause of epilepsy and can be detected through magnetic resonance (MR) imaging. Several earlier studies have focused on computing the gradient magnitude of the MR image and used the resulting map to model the blurred gray/white matter junction. However, gradient magnitude cannot quantify the blurred gray/white matter junction. Therefore, we proposed a novel algorithm called local directional probability optimization (LDPO) for detecting and quantifying the width of the gray/white matter boundary (GWB) within the lesional areas. The proposed LDPO method mainly consists of the following three stages: (1) introduction of a hidden Markov random field-expectation-maximization algorithm to compute the probability images of brain tissues in order to obtain the GWB region; (2) generation of local directions from gray matter (GM) to white matter (WM) passing through the GWB, considering the GWB to be an electric potential field; (3) determination of the optimal local directions for any given voxel of GWB, based on iterative searching of the neighborhood. This was then used to measure the width of the GWB. The proposed LDPO method was tested on real MR images of patients with FCD lesions. The results indicated that the LDPO method could quantify the GWB width. On the GWB width map, the width of the blurred GWB in the lesional region was observed to be greater than that in the non-lesional regions. The proposed GWB width map produced higher F-scores in terms of detecting the blurred GWB within the FCD lesional region as compared to that of FCD feature maps, indicating better trade-off between precision and recall.

High sensitivity plasmonic sensor using hybrid structure of graphene stripe combined with gold gap-ring

Abstract: Local surface plasmonic resonance (LSPR) produced by metallic nano-structures is often sensitive to the refractive index of the surrounding media and can be applied for sensing. However, it often suffers from large line width caused by large plasmonic radiative damping, especially in the infrared (IR) frequencies, which reduces the sensitivity. Here we propose a hybrid structure consists of a graphene stripe and a gold gap-ring at short-IR frequencies (1–3 µm). Due to the low loss and high plasmonic confinement of graphene, LSPR line width of 6 nm is obtained. In addition, due to the strong coupling of the gold gap-ring with graphene stripe, the intensity of graphene LSPR is enhanced by 100 times. Simulation results show that the sensitivity of the sensor is ~1000 nm/RIU (refractive index unit) and the figure of merit (FoM) can reach up to 383.

Cerebral vascular enhancement using a weighted 3D symmetry filter

Abstract: The automated detection of cerebral vessels is of great importance in understanding of the diagnosis, treatment and mechanism of many brain vascular pathologies. However, automatic vessel detection from 3D angiography continues to be an open issue. In this paper we introduce a novel 3D symmetry filter that has excellent performance on enhancing vessels in magnetic resonance angiography (MRA). The proposed filter not only takes into account of local phase features estimated by using a quadrature filter so as to distinguish between lines and the edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance in the position of the respective contours. As a result this filter can produce a strong response to the vascular features despite variations in scale, contrast, and bifurcations in images. Our results demonstrate its superior performance to other state-of-the-art methods.

Endoscopic surgery navigation method and system

Abstract: Disclosed are an endoscopic surgery navigation method and system. The method comprises: reading multi-modal medical image data (101); using any image data in the multi-modal medical image data as a reference image, and using other image data as an image to be registered, so as to perform image full affine matching (102); performing scene reconstruction and hybrid rendering on the image data after the image full affine matching, and obtaining a virtual scene (103); completing a registration between a CT image and a pose of a patient by means of a convex hull fast registration method; performing fast calibration by means of a surface point cloud subjected to convex hull optimization; tracing an endoscope and surgical tools, and obtaining a pose relationship between the endoscope and surgical tools and the body of the patient (105); according to the obtained pose relationship, obtaining a virtual scene view of the endoscope in the virtual scene (106); and performing Gaussian function decay on an edge of a real-time endoscope image, and fusing same with the virtual scene view of the endoscope, so as to realize layered rendering of the scene (107). The method and system accelerate the rendering speed of an image, and improve the navigation accuracy.