3D far-field Lidar sensing and computational modeling for human identification.

3D sensors offer depth sensing that may be used for task-specific data processing and computational modeling. Many existing methods for human identification using 3D depth sensors primarily focus on Kinect data, where the range is very limited. This work considers a 3D long-range Lidar sensor for far-field imaging of human subjects in 3D Lidar full motion video (FMV) of "walking" action. 3D Lidar FMV data for human subjects are used to develop computational modeling for automated human silhouette and skeleton extraction followed by subject identification. We propose a matrix completion algorithm to handle missing data in 3D FMV due to self-occlusion and occlusion from other subjects for 3D skeleton extraction. We further study the effect of noise in the 3D low resolution far-field Lidar data in human silhouette extraction performance of the model. Moreover, this work addresses challenges associated with far-field 3D Lidar including learning with a limited amount of data and low resolution. Moreover, we evaluate the proposed computational algorithm using a gallery of 10 subjects for human identification and show that our method is competitive with the state-of-the-art OpenPose and V2VPose skeleton extraction models using the same dataset for human identification.

Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead.

Electron cryomicroscopy (cryoEM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. CryoEM has been successfully used to visualize macromolecular complexes such as ribosomes, viruses, and ion channels. Determination of structural models of these at various conformational states leads to insight on how these molecules function. Recent advances in imaging technology have given cryoEM a scientific rebirth. As a result of these technological advances image processing and analysis have yielded molecular structures at atomic resolution. Nevertheless there continue to be challenges in image processing, and in this article we will touch on the most essential in order to derive an accurate three-dimensional model from noisy projection images. Traditional approaches, such as k-means clustering for class averaging, will be provided as background. We will then highlight new approaches for each image processing subproblem, including a 3D reconstruction method for asymmetric molecules using just two projection images and deep learning algorithms for automated particle picking.

Robust cardiomyocyte-specific gene expression following systemic injection of AAV: in vivo gene delivery follows a Poisson distribution.

Newly isolated serotypes of AAV readily cross the endothelial barrier to provide efficient transgene delivery throughout the body. However, tissue-specific expression is preferred in most experimental studies and gene therapy protocols. Previous efforts to restrict gene expression to the myocardium often relied on direct injection into heart muscle or intracoronary perfusion. Here, we report an AAV vector system employing the cardiac troponin T (cTnT) promoter. Using luciferase and enhanced green fluorescence protein (eGFP), the efficiency and specificity of cardiac reporter gene expression using AAV serotype capsids: AAV-1, 2, 6, 8 or 9 were tested after systemic administration to 1-week-old mice. Luciferase assays showed that the cTnT promoter worked in combination with each of the AAV serotype capsids to provide cardiomyocyte-specific gene expression, but AAV-9 followed closely by AAV-8 was the most efficient. AAV9-mediated gene expression from the cTnT promoter was 640-fold greater in the heart compared with the next highest tissue (liver). eGFP fluorescence indicated a transduction efficiency of 96% using AAV-9 at a dose of only 3.15 × 10(10) viral particles per mouse. Moreover, the intensity of cardiomyocyte eGFP fluorescence measured on a cell-by-cell basis revealed that AAV-mediated gene expression in the heart can be modeled as a Poisson distribution, requiring an average of nearly two vector genomes per cell to attain an 85% transduction efficiency.

Selectin ligands promote ultrasound contrast agent adhesion under shear flow.

Contrast-enhanced ultrasound imaging has shown promise in the field of molecular imaging. This technique relies upon the adhesion of ultrasound contrast agent (UCA) to targeted molecular markers of disease. This is accomplished by coating the surface of the contrast agent with a ligand that specifically binds to the intended molecular marker. Most UCA particles remain in the blood space, and their retention is influenced by the forces imposed by blood flow. For a UCA bound to a molecular target on the vascular endothelium, blood flow imposes a dislodging force that counteracts retention. Additionally, contrast agent adhesion to the molecular marker requires rapid binding kinetics, especially in rapid blood flow. The ability of a ligand:target bond complex to mediate fast adhesion and withstand dislodging force is necessary for efficient ultrasound-based molecular imaging. In the current study, we describe a flow-based adhesion assay which, combined with a novel automated tracking algorithm, enables quick determination of the ability of a targeting ligand to mediate effective contrast agent adhesion. This system was used to explore the adhesion of UCA targeted to the proinflammatory endothelial protein P-selectin via four targeting ligands, which revealed several interesting adhesive behaviors. Contrast agents targeted with glycoconjugate ligands modeled on P-selectin glycoprotein ligand 1 exhibited primarily unstable or transient adhesion, while UCA targeted with an anti-P-selectin monoclonal antibody exhibited primarily firm adhesion, although the efficiency with which these agents were recruited to the target surface was relatively low.

Scale space classification using area morphology.

We explore the application of area morphology to image classification. From the input image, a scale space is created by successive application of an area morphology operator. The pixels within the scale space corresponding to the same image location form a scale space vector. A scale space vector therefore contains the intensity of a particular pixel for a given set of scales, determined in this approach by image granulometry. Using the standard k-means algorithm or the fuzzy c-means algorithm, the image pixels can be classified by clustering the associated scale space vectors. The scale space classifier presented here is rooted in the novel area open-close and area close-open scale spaces. Unlike other scale generating filters, the area operators affect the image by removing connected components within the image level sets that do not satisfy the minimum area criterion. To show that the area open-close and area close-open scale spaces provide an effective multiscale structure for image classification, we demonstrate the fidelity, causality, and edge localization properties for the scale spaces. The analysis also reveals that the area open-close and area close-open scale spaces improve classification by clustering members of similar objects more effectively than the fixed scale classifier. Experimental results are provided that demonstrate the reduction in intra-region classification error and in overall classification error given by the scale space classifier for classification applications where object scale is important. In both visual and objective comparisons, the scale space approach outperforms the traditional fixed scale clustering algorithms and the parametric Bayesian classifier for classification tasks that depend on object scale.

Piecewise and local image models for regularized image restoration using cross-validation.

We describe two broad classes of useful and physically meaningful image models that can be used to construct novel smoothing constraints for use in the regularized image restoration problem. The two classes, termed piecewise image models (PIMs) and focal image models (LIMs), respectively, capture unique image properties that can be adapted to the image and that reflect structurally significant surface characteristics. Members of the PIM and LIM classes are easily formed into regularization operators that replace differential-type constraints. We also develop an adaptive strategy for selecting the best PIM or LIM for a given problem (from among the defined class), and we explain the construction of the corresponding regularization operators. Considerable attention is also given to determining the regularization parameter via a cross-validation technique, and also to the selection of an optimization strategy for solving the problem. Several results are provided that illustrate the processes of model selection, parameter selection, and image restoration. The overall approach provides a new viewpoint on the restoration problem through the use of new image models that capture salient image features that are not well represented through traditional approaches.

Multigrid anisotropic diffusion.

A multigrid anisotropic diffusion algorithm for image processing is presented. The multigrid implementation provides an efficient hierarchical relaxation method that facilitates the application of anisotropic diffusion to time-critical processes. Through a multigrid V-cycle, the anisotropic diffusion equations are successively transferred to coarser grids and used in a coarse-to-fine error correction scheme. When a coarse grid with a trivial solution is reached, the coarse grid estimates of the residual error can be propagated to the original grid and used to refine the solution. The main benefits of the multigrid approach are rapid intraregion smoothing and reduction of artifacts due to the elimination of low-frequency error. The theory of multigrid anisotropic diffusion is developed. Then, the intergrid transfer functions, relaxation techniques, diffusion coefficients, and boundary conditions are discussed. The analysis includes the examination of the storage requirements, the computational cost, and the solution quality. Finally, experimental results are reported that demonstrate the effectiveness of the multigrid approach.

Nonlinear image estimation using piecewise and local image models.

We introduce a new approach to image estimation based on a flexible constraint framework that encapsulates meaningful structural image assumptions. Piecewise image models (PIMs) and local image models (LIMs) are defined and utilized to estimate noise-corrupted images, PIMs and LIMs are defined by image sets obeying certain piecewise or local image properties, such as piecewise linearity, or local monotonicity. By optimizing local image characteristics imposed by the models, image estimates are produced with respect to the characteristic sets defined by the models. Thus, we propose a new general formulation for nonlinear set-theoretic image estimation. Detailed image estimation algorithms and examples are given using two PIMs: piecewise constant (PICO) and piecewise linear (PILI) models, and two LIMs: locally monotonic (LOMO) and locally convex/concave (LOCO) models. These models define properties that hold over local image neighborhoods, and the corresponding image estimates may be inexpensively computed by iterative optimization algorithms. Forcing the model constraints to hold at every image coordinate of the solution defines a nonlinear regression problem that is generally nonconvex and combinatorial. However, approximate solutions may be computed in reasonable time using the novel generalized deterministic annealing (GDA) optimization technique, which is particularly well suited for locally constrained problems of this type. Results are given for corrupted imagery with signal-to-noise ratio (SNR) as low as 2 dB, demonstrating high quality image estimation as measured by local feature integrity, and improvement in SNR.

Generalized deterministic annealing.

We develop a general formalism for computing high quality, low-cost solutions to nonconvex combinatorial optimization problems expressible as distributed interacting local constraints. For problems of this type, generalized deterministic annealing (GDA) avoids the performance-related sacrifices of current techniques. GDA exploits the localized structure of such problems by assigning K-state neurons to each optimization variable. The neuron values correspond to the probability densities of K-state local Markov chains and may be updated serially or in parallel; the Markov model is derived from the Markov model of simulated annealing (SA), although it is greatly simplified. Theorems are presented that firmly establish the convergence properties of GDA, as well as supplying practical guidelines for selecting the initial and final temperatures in the annealing process. A benchmark image enhancement application is provided where the performance of GDA is compared to other optimization methods. The empirical data taken in conjunction with the formal analytical results suggest that GDA enjoys significant performance advantages relative to current methods for combinatorial optimization.