3D Spine Model Reconstruction Based on RGBD Images of Unclothed Back Surface.

Most 3D spine reconstruction methods require X-ray images as input, which usually leads to high cost and radiation damage. Therefore, these methods are hardly ever applied to large scale scoliosis screening or spine pose monitoring during treatment. We propose a novel, low-cost, easy-to-operate and none-radioactive 3D spine model reconstruction method, which is based on human back surface information without requiring X-ray images as input. Our method fits a pre-built Spine Priors Model (SPrM) to human back surface information and reconstructs the main part of spine with 17 vertebrae: lumbar vertebrae L1-L5 and thoracic vertebrae T1-T12. The Spine Priors Model is constructed according to human spine priors, including Statistical Spine Shape Model (SSSM), Spine Pose Model (SPM) and Spine Biomechanical Simplified Model (SBSM). The spine-related information on back surface, including back surface spinous curve and local symmetry nearby spinous curve is extracted from the RGBD images of human back surface. We formulate the spine optimization constraints from spine-related feature on back surface and spine priors, then optimize the spine model by gradient descent to get the optimal personalized shape parameters and pose parameters of the Spine Priors Model (SPrM). We assess our reconstruction by scoliosis Cobb angle error, and the result is comparable to current X-ray based methods.

The mediating effects of depression, anxiety, and rapid eye movement sleep behavior disorder on the association between dopaminergic replacement therapy and impulse control disorders in Parkinson's disease.

OBJECTIVES: This study aims to longitudinally explore whether and how rapid eye movement sleep behavior disorder (RBD), depression, and anxiety mediate the association between dopaminergic replacement therapy (DRT) and impulse control disorders (ICDs) in patients with Parkinson's disease (PD). METHODS: Subjects were selected from the Parkinson's Progression Markers Initiative. After excluding missing data, 268, 223, 218, 238, and 219 patients with PD diagnosed at 12, 24, 36, 48, and 60 months prior, respectively, were included. We used the Questionnaire for Impulsive-Compulsive Disorders, RBD Screening Questionnaire, Geriatric Depression Scale, and State-Trait-Anxiety Inventory to assess ICBs, RBD, depression, and anxiety, respectively. We constructed three causal mediation analysis models to infer potential contingent pathways from DRT to ICD mediated by depression, anxiety, and RBD separately. RESULTS: DRT was associated with an increased risk of PD incidence. Aggravation of ICDs was partly explained by improvements in depression (the average causal mediation effect accounted for 8.0% of the total effect) and RBD (the average causal mediation effect of RBD accounted for 16.4% of the total effect). This suggested that anxiety (the average causal mediation effect accounted for 12.7% of the total effect) plays a mediating role. CONCLUSIONS: Focusing on changes in RBD, depression, and anxiety associated with hyperdopaminergic status should be an essential part of strategies to prevent ICDs in patients with Parkinson's disease.

Highlight Removal of Multi-View Facial Images.

Highlight removal is a fundamental and challenging task that has been an active field for decades. Although several methods have recently been improved for facial images, they are typically designed for a single image. This paper presents a lightweight optimization method for removing the specular highlight reflections of multi-view facial images. This is achieved by taking full advantage of the Lambertian consistency, which states that the diffuse component does not vary with the change in the viewing angle, while the specular component changes the behavior. We provide non-negative constraints on light and shading in all directions, rather than normal directions contained in the face, to obtain physically reliable properties. The removal of highlights is further facilitated through the estimation of illumination chromaticity, which is done by employing orthogonal subspace projection. An important practical feature of the proposed method does not require face reflectance priors. A dataset with ground truth for highlight removal of multi-view facial images is captured to quantitatively evaluate the performance of our method. We demonstrate the robustness and accuracy of our method through comparisons to existing methods for removing specular highlights and improvement in applications such as reconstruction.

Longitudinal Mediating Effect of Depression on the Relationship between Excessive Daytime Sleepiness and Activities of Daily Living in Parkinson's Disease.

OBJECTIVES: Whether depression affects activities of daily living (ADLs) in patients with Parkinson's disease (PD) via excessive daytime sleepiness (EDS) remains unclear; moreover, few longitudinal studies have been conducted. METHODS: We recruited 421 patients from the Parkinson's Progression Markers Initiative. We constructed a latent growth mediation model to explore the longitudinal mediating effect of depression on the relationship between EDS and ADLs. RESULTS: EDS (p < .001) and depression scores (p < .001) both increased, and ADL scores (p < .001) decreased. Moreover, EDS was positively correlated with depression, whereas an increase in EDS significantly reduced ADLs. The initial value (95% confidence interval [CI]: 0.026, 0.154) and the rate of change (95% CI: 0.138, 0.514) of self-reported depression measured using the Geriatric Depression Scale(GDS) partially mediated the association between EDS and ADL score. CONCLUSIONS: The indirect effect of the longitudinal changes of depression on the relationship between EDS and ADLs highlights the importance of depression changes in PD patients with EDS. CLINICAL IMPLICATIONS: Depression should be considered a mediator by clinicians; preventing the worsening of depression is essential for improving ADLs in patients with PD, especially those with EDS.

The path linking excessive daytime sleepiness and activity of daily living in Parkinson's disease: the longitudinal mediation effect of autonomic dysfunction.

BACKGROUND: Excessive daytime sleepiness (EDS) and autonomic dysfunction have been verified to impair activity of daily living (ADL) in patients with Parkinson's disease (PD). Whether EDS can affect ADL in PD patients through autonomic dysfunction is still unclear. The purpose of this study is to explore the longitudinal mediation effect of autonomic dysfunction between EDS and ADL. METHODS: Data used in this study were from six-follow-up visits of 413 patients with newly diagnosed PD from the Parkinson's Progression Markers Initiative (PPMI). We used latent growth mediation modeling (LGMM) to explore whether the autonomic dysfunction is a longitudinal mediator between EDS and ADL. RESULTS: The results showed that as the disease progresses, EDS (P < 0.001) and autonomic dysfunction (P < 0.001) gradually worsened and ADL (P < 0.001) gradually decreased in PD patients. In addition, the more severe the patients' EDS symptom, the more worsened the symptoms of autonomic dysfunction, which result in a decrease in ADL. Both the intercept (95% CI: 0.142, 0.308) and the slope (95% CI: 0.083, 0.331) of autonomic dysfunction showed a partial mediating effect, and a longitudinal mediation effect was presented. CONCLUSION: Longitudinal changes in EDS affect the ADL of PD patients directly or indirectly by affecting the symptoms of autonomic dysfunction. Controlling the symptoms of autonomic dysfunction may improve the ADL of PD patients with EDS.

Depth-based removal of thermal reflection with the light-field theory.

Thermal imaging is a useful imaging technique in many scenarios. It can capture the temperature distribution of scenes in the dark and see through sparse smoke and dust. However, some surfaces such as steel and glass with high reflectivity lead to a reflection problem in thermal imaging, while heavy mist and gases lead to the occlusion problem. We proposed an efficient algorithm to solve the occlusion problem in our earlier work. The reflection in thermal images causes errors in detection and temperature measurement. Therefore, the precise model and efficient algorithms to solve this problem are in high demand. In this paper, we mainly model the reflection problem in thermal imaging and propose an algorithm to deal with it. In our experiments, a thermal camera array is built to capture the thermal light-field images. We first separate a part of the reflection pixels from thermal images based on the depth information. After that, the thermal reflection is removed by optimizing a designed cost function. The experiment results show that our reflection removal method can separate the thermal reflection with high precision, retain the objects in the scene, and get better performance than existing methods.

Global disassortative rewiring strategy for enhancing the robustness of scale-free networks against localized attack.

The robustness of complex networks against attack has been an important issue for decades. Most of the previous studies focused on targeted attack (TA) and random attack (RA), while recently localized attack (LA) has drawn the attention of researchers. However, the existing studies related to LA mainly aim to reveal the properties on various network topologies so that the strategy to enhance network robustness against LA is still not well studied. In this paper, we propose a global disassortative rewiring strategy to enhance the robustness of scale-free networks against LA without changing the degree distribution. The validations are conducted on simulated scale-free networks and two real-life networks. As global disassortative rewiring strategy outperforms the other strategies, it can be proved effective in enhancing network robustness against LA and may contribute to future network risk reduction. In addition, by avoiding calculating and comparing the localized-robustness measurement within each rewire operation, our strategy offers a significant advantage in computational efficiency.

Reconstruction of High-Precision Semantic Map.

We present a real-time Truncated Signed Distance Field (TSDF)-based three-dimensional (3D) semantic reconstruction for LiDAR point cloud, which achieves incremental surface reconstruction and highly accurate semantic segmentation. The high-precise 3D semantic reconstruction in real time on LiDAR data is important but challenging. Lighting Detection and Ranging (LiDAR) data with high accuracy is massive for 3D reconstruction. We so propose a line-of-sight algorithm to update implicit surface incrementally. Meanwhile, in order to use more semantic information effectively, an online attention-based spatial and temporal feature fusion method is proposed, which is well integrated into the reconstruction system. We implement parallel computation in the reconstruction and semantic fusion process, which achieves real-time performance. We demonstrate our approach on the CARLA dataset, Apollo dataset, and our dataset. When compared with the state-of-art mapping methods, our method has a great advantage in terms of both quality and speed, which meets the needs of robotic mapping and navigation.

Towards Clinical Translation of LED-Based Photoacoustic Imaging: A Review.

Photoacoustic imaging, with the capability to provide simultaneous structural, functional, and molecular information, is one of the fastest growing biomedical imaging modalities of recent times. As a hybrid modality, it not only provides greater penetration depth than the purely optical imaging techniques, but also provides optical contrast of molecular components in the living tissue. Conventionally, photoacoustic imaging systems utilize bulky and expensive class IV lasers, which is one of the key factors hindering the clinical translation of this promising modality. Use of LEDs which are portable and affordable offers a unique opportunity to accelerate the clinical translation of photoacoustics. In this paper, we first review the development history of LED as an illumination source in biomedical photoacoustic imaging. Key developments in this area, from point-source measurements to development of high-power LED arrays, are briefly discussed. Finally, we thoroughly review multiple phantom, ex-vivo, animal in-vivo, human in-vivo, and clinical pilot studies and demonstrate the unprecedented preclinical and clinical potential of LED-based photoacoustic imaging.

Modeling the occlusion problem in thermal imaging to allow seeing through mist and foliage.

Thermal imaging can easily see through smoke and dust. It is a useful technique in the military and industrial fields. However, thermal imaging can also be blocked by heavy mist or gases with high emissivity such as CO2. Allowing a thermal camera to see through these obstacles is in high demand. In this paper, we modeled the occlusion problem in thermal imaging and proposed an algorithm to image the objects through mist and foliage. We built a system to capture the thermal light field camera. We took thermal reflection and absorption of the obstacles into consideration. We removed the obstacle part in thermal images by estimating the intensity of infrared radiation. Then, we refocused the thermal images on the specific depth of the object for reconstruction. The experiment's results show that a proposed algorithm can reconstruct the occluded objects in a clear shape while blurring the obstacles. Based on the thermal occlusion model and refocusing, the thermal camera can image a human through mist and foliage.

Illumination separation of non-Lambertian scenes from a single hyperspectral image.

In this paper, we propose a general framework to estimate the spectrum of the illumination from global specular information in a single hyperspectral image. By utilizing the specular independent subspace, we iteratively separate the reflectance components and shape a weight scheme in order to find specular-contaminated pixels. After that, the illumination can be directly estimated by factorizing the weighted specular-contaminated pixels. The proposed method enables a direct and effective decomposition of the illumination and reflectance components from a single hyperspectral image. We demonstrate the robustness and accuracy of our method on simulation and real experiments. Moreover, we capture a hyperspectral image dataset with ground-truth illumination to quantitative compare the performance.

Measure and model a 3-D space-variant PSF for fluorescence microscopy image deblurring.

Conventional deconvolution methods assume that the microscopy system is spatially invariant, introducing considerable errors. We developed a method to more precisely estimate space-variant point-spread functions from sparse measurements. To this end, a space-variant version of deblurring algorithm was developed and combined with a total-variation regularization. Validation with both simulation and real data showed that our PSF model is more accurate than the piecewise-invariant model and the blending model. Comparing with the orthogonal basis decomposition based PSF model, our proposed model also performed with a considerable improvement. We also evaluated the proposed deblurring algorithm. Our new deblurring algorithm showed a significantly better signal-to-noise ratio and higher image quality than those of the conventional space-invariant algorithm.

Intrinsic decomposition from a single spectral image.

In this paper, we present a spectral intrinsic image decomposition (SIID) model, which is dedicated to resolve a natural scene into its purely independent intrinsic components: illumination, shading, and reflectance. By introducing spectral information, our work can solve many challenging cases, such as scenes with metameric effects, which are hard to tackle for trichromatic intrinsic image decomposition (IID), and thus offers potential benefits to many higher-level vision tasks, e.g., materials classification and recognition, shape-from-shading, and spectral image relighting. A both effective and efficient algorithm is presented to decompose a spectral image into its independent intrinsic components. To facilitate future SIID research, we present a public dataset with ground-truth illumination, shading, reflectance and specularity, and a meaningful error metric, so that the quantitative comparison becomes achievable. The experiments on this dataset and other images demonstrate the accuracy and robustness of the proposed method on diverse scenes, and reveal that more spectral channels indeed facilitate the vision task (i.e., segmentation and recognition).

Graph-regularized tensor robust principal component analysis for hyperspectral image denoising.

In this paper, we have developed a novel model that is named graph-regularized tensor robust principal component analysis (GTRPCA) for denoising hyperspectral images (HSIs). Incorporating spectral graph regularization into TRPCA makes the model more accurate by preserving local geometric structures embedded in a high-dimensional space. Based on tensor singular value decomposition (t-SVD), we introduce a general tensor-based altering direction method of multipliers (ADMM) algorithm which can solve the proposed model for denoising HSIs. Experiments on both the synthetic and real captured datasets have demonstrated the effectiveness of the proposed method.

Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine.

The morphology of dendritic spines is highly correlated with the neuron function. Therefore, it is of positive influence for the research of the dendritic spines. However, it is tried to manually label the spine types for statistical analysis. In this work, we proposed an approach based on the combination of wavelet contour analysis for the backbone detection, wavelet packet entropy, and fuzzy support vector machine for the spine classification. The experiments show that this approach is promising. The average detection accuracy of "MushRoom" achieves 97.3%, "Stubby" achieves 94.6%, and "Thin" achieves 97.2%.

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

AIM: This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. METHOD: Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. RESULTS: The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. CONCLUSION: Our method performs better than Gorji's approach and five other state-of-the-art approaches.

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning.

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine.

Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm-RTSVM- which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research.

A new method for quantifying mitochondrial axonal transport.

Axonal transport of mitochondria is critical for neuronal survival and function. Automatically quantifying and analyzing mitochondrial movement in a large quantity remain challenging. Here, we report an efficient method for imaging and quantifying axonal mitochondrial transport using microfluidic-chamber-cultured neurons together with a newly developed analysis package named "MitoQuant". This tool-kit consists of an automated program for tracking mitochondrial movement inside live neuronal axons and a transient-velocity analysis program for analyzing dynamic movement patterns of mitochondria. Using this method, we examined axonal mitochondrial movement both in cultured mammalian neurons and in motor neuron axons of Drosophila in vivo. In 3 different paradigms (temperature changes, drug treatment and genetic manipulation) that affect mitochondria, we have shown that this new method is highly efficient and sensitive for detecting changes in mitochondrial movement. The method significantly enhanced our ability to quantitatively analyze axonal mitochondrial movement and allowed us to detect dynamic changes in axonal mitochondrial transport that were not detected by traditional kymographic analyses.

Dynamic Human Body Modeling Using a Single RGB Camera.

In this paper, we present a novel automatic pipeline to build personalized parametric models of dynamic people using a single RGB camera. Compared to previous approaches that use monocular RGB images, our system can model a 3D human body automatically and incrementally, taking advantage of human motion. Based on coarse 2D and 3D poses estimated from image sequences, we first perform a kinematic classification of human body parts to refine the poses and obtain reconstructed body parts. Next, a personalized parametric human model is generated by driving a general template to fit the body parts and calculating the non-rigid deformation. Experimental results show that our shape estimation method achieves comparable accuracy with reconstructed models using depth cameras, yet requires neither user interaction nor any dedicated devices, leading to the feasibility of using this method on widely available smart phones.