Myungjoo Kang

Implicit and Nonparametric Shape Reconstruction from Unorganized Data Using a Variational Level Set Method

In this paper we consider a fundamental visualization problem: shape reconstruction from an unorganized data set. A new minimal-surface-like model and its variational and partial differential equation (PDE) formulation are introduced. In our formulation only distance to the data set is used as our input. Moreover, the distance is computed with optimal speed using a new numerical PDE algorithm. The data set can include points, curves, and surface patches. Our model has a natural scaling in the nonlinear regularization that allows flexibility close to the data set while it also minimizes oscillations between data points. To find the final shape, we continuously deform an initial surface following the gradient flow of our energy functional. An offset (an exterior contour) of the distance function to the data set is used as our initial surface. We have developed a new and efficient algorithm to find this initial surface. We use the level set method in our numerical computation in order to capture the deformation of the initial surface and to find an implicit representation (using the signed distance function) of the final shape on a fixed rectangular grid. Our variational/PDE approach using the level set method allows us to handle complicated topologies and noisy or highly nonuniform data sets quite easily. The constructed shape is smoother than any piecewise linear reconstruction. Moreover, our approach is easily scalable for different resolutions and works in any number of space dimensions.

Three-Dimensional Evaluation of the Lamina Cribrosa Using Spectral-Domain Optical Coherence Tomography in Glaucoma

PURPOSE To introduce a novel, digital, three-dimensional (3D) reconstruction of the optic nerve head (ONH) and to use this method to evaluate the 3D configuration of the lamina cribrosa (LC) in patients with primary open-angle glaucoma. METHODS Optic discs of 137 eyes of 137 patients with open-angle glaucoma were scanned with enhanced depth-imaging spectral domain-optical coherence tomography (SD-OCT). 3D images of the ONH were then reconstructed from B-scan images using maximum intensity projection (MIP) and texture-based volume rendering (VRT). The performance of the threshold segmentation by MIP and VRT was assessed by comparing the distance of the anterior LC surface from the reference line set at the Bruchs membrane opening level (LC depth) measured within both of the 3D images and the B-scan images. RESULTS The LC configuration could be evaluated three dimensionally in ∼95% of patients scanned with enhanced depth-imaging SD-OCT. The mean LC depth was 559.50 ± 151.98, 558.97 ± 152.39, and 560.22 ± 152.26 μm in B-scan, MIP, and VRT images, respectively. There were excellent agreements between the values (intraclass correlation coefficient = 1.000 between MIP and B-scan, and 0.999 between VRT and B-scan). The configuration of the LC varied considerably among individual glaucoma patients. CONCLUSIONS This method provides measurable 3D images of the LC that enable comprehensive evaluation of the LC configuration. This technique should facilitate the investigation of the LC in glaucomatous eyes.

Non-convex hybrid total variation for image denoising

Image restoration problems, such as image denoising, are important steps in various image processing method, such as image segmentation and object recognition. Due to the edge preserving property of the convex total variation (TV), variational model with TV is commonly used in image restoration. However, staircase artifacts are frequently observed in restored smoothed region. To remove the staircase artifacts in smoothed region, convex higher-order TV (HOTV) regularization methods are introduced. But the valuable edge information of the image is also attenuated. In this paper, we propose non-convex hybrid TV regularization method to significantly reduce staircase artifacts while well preserving the valuable edge information of the image. To efficiently find a solution of the variation model with the proposed regularizer, we use the iterative reweighted method with the augmented Lagrangian based algorithm. The proposed model shows the best performance in terms of the signal-to-noise ratio (SNR) and the structure similarity index measure (SSIM) with comparable computational complexity.

Ultra-Wideband Multi-Dye-Sensitized Upconverting Nanoparticles for Information Security Application

Multi-dye-sensitized upconverting nanoparticles (UCNPs), which harvest photons of wide wavelength range (450-975 nm) are designed and synthesized. The UCNPs embedded in a photo-acid generating layer are integrated on destructible nonvolatile resistive memory device. Upon illumination of light, the system permanently erases stored data, achieving enhanced information security.

An image-based algorithm for precise and accurate high throughput assessment of drug activity against the human parasite Trypanosoma cruzi.

We present a customized high content (image-based) and high throughput screening algorithm for the quantification of Trypanosoma cruzi infection in host cells. Based solely on DNA staining and single-channel images, the algorithm precisely segments and identifies the nuclei and cytoplasm of mammalian host cells as well as the intracellular parasites infecting the cells. The algorithm outputs statistical parameters including the total number of cells, number of infected cells and the total number of parasites per image, the average number of parasites per infected cell, and the infection ratio (defined as the number of infected cells divided by the total number of cells). Accurate and precise estimation of these parameters allow for both quantification of compound activity against parasites, as well as the compound cytotoxicity, thus eliminating the need for an additional toxicity-assay, hereby reducing screening costs significantly. We validate the performance of the algorithm using two known drugs against T.cruzi: Benznidazole and Nifurtimox. Also, we have checked the performance of the cell detection with manual inspection of the images. Finally, from the titration of the two compounds, we confirm that the algorithm provides the expected half maximal effective concentration (EC50) of the anti-T. cruzi activity.

On Boundary Condition Capturing for Multiphase Interfaces

This review paper begins with an overview of the boundary condition capturing approach to solving problems with interfaces. Although the authors’ original motivation was to extend the ghost fluid method from compressible to incompressible flow, the elliptic nature of incompressible flow quickly quenched the idea that ghost cells could be defined and used in the usual manner. Instead the boundary conditions had to be implicitly captured by the matrix formulation itself, leading to the novel approach. We first review the work on the variable coefficient Poisson equation, noting that the simplicity of the method allowed for an elegant convergence proof. Simplicity and robustness also allowed for a quick extension to three-dimensional two-phase incompressible flows including the effects of viscosity and surface tension, which is discussed subsequently. The method has enjoyed popularity in both computational physics and computer graphics, and we show some comparisons with the traditional delta function approach for the visual simulation of bubbles. Finally, we discuss extensions to problems where the velocity is discontinuous as well, as is the case for premixed flames, and show an example of multiple interacting liquids that includes all of the aforementioned phenomena.

An Image Analysis Algorithm for Malaria Parasite Stage Classification and Viability Quantification

With more than 40% of the world’s population at risk, 200–300 million infections each year, and an estimated 1.2 million deaths annually, malaria remains one of the most important public health problems of mankind today. With the propensity of malaria parasites to rapidly develop resistance to newly developed therapies, and the recent failures of artemisinin-based drugs in Southeast Asia, there is an urgent need for new antimalarial compounds with novel mechanisms of action to be developed against multidrug resistant malaria. We present here a novel image analysis algorithm for the quantitative detection and classification of Plasmodium lifecycle stages in culture as well as discriminating between viable and dead parasites in drug-treated samples. This new algorithm reliably estimates the number of red blood cells (isolated or clustered) per fluorescence image field, and accurately identifies parasitized erythrocytes on the basis of high intensity DAPI-stained parasite nuclei spots and Mitotracker-stained mitochondrial in viable parasites. We validated the performance of the algorithm by manual counting of the infected and non-infected red blood cells in multiple image fields, and the quantitative analyses of the different parasite stages (early rings, rings, trophozoites, schizonts) at various time-point post-merozoite invasion, in tightly synchronized cultures. Additionally, the developed algorithm provided parasitological effective concentration 50 (EC50) values for both chloroquine and artemisinin, that were similar to known growth inhibitory EC50 values for these compounds as determined using conventional SYBR Green I and lactate dehydrogenase-based assays.

Multiple-Region Segmentation Without Supervision by Adaptive Global Maximum Clustering

In this paper, we propose a new method of segmenting an image into several sets of pixels with similar intensity values called regions. A multiple-region segmentation problem is unstable because the result considerably depends on the number of regions given a priori. Therefore, one of the most important tasks in solving the problem is automatically finding the number of regions. The method we propose is able to find the reasonable number of distinct regions not only for clean images but also for noisy ones. Our method is made up of two procedures. First, we develop the adaptive global maximum clustering. In this procedure, we deal with an image histogram and automatically obtain the number of significant local maxima of the histogram. This number indicates the number of different regions in the image. Second, we derive a simple and fast calculation to segment an image composed of distinct multiple regions. Then, we split an image into multiple regions according to the previous procedure. Finally, we show the efficiency of our method by comparing it with other previous methods.

Efficient Nonsmooth Nonconvex Optimization for Image Restoration and Segmentation

In this article, we introduce variational image restoration and segmentation models that incorporate the