Qing Yang

Segmentation of heterogeneous blob objects through voting and level set formulation

Blob-like structures occur often in nature, where they aid in cueing and the pre-attentive process. These structures often overlap, form perceptual boundaries, and are heterogeneous in shape, size, and intensity. In this paper, voting, Voronoi tessellation, and level set methods are combined to delineate blob-like structures. Voting and subsequent Voronoi tessellation provide the initial condition and the boundary constraints for each blob, while curve evolution through level set formulation provides refined segmentation of each blob within the Voronoi region. The paper concludes with the application of the proposed method to a dataset produced from cell based fluorescence assays and stellar data.

BioSig: an imaging bioinformatic system for studying phenomics

Using genomic information to understand complex organisms requires comprehensive knowledge of the dynamics of phenotype generation and maintenance. A phenotype results from selective expression of the genome, creating a history of the cell and its response to the extracellular environment. Defining cell phenomes requires tracking the kinetics and quantities of multiple constituent proteins, their cellular context, and their morphological features in large populations. The paper considers how the BioSig imaging bioinformatic system for characterizing phenomics answers these challenges. The BioSig approach to microscopy and quantitative image analysis helps to build a more detailed picture of the signaling that occurs between cells as a response to exogenous stimulus such as radiation or as a consequence of endogenous programs leading to biological functions. The system provides a data model for capturing experimental annotations and variables, computational techniques for summarizing large numbers of images, and a distributed architecture that facilitates distant collaboration.

BioSig: an imaging bioinformatics system for phenotypic analysis

Organisms express their genomes in a cell-specific manner, resulting in a variety of cellular phenotypes or phenomes. Mapping cell phenomes under a variety of experimental conditions is necessary in order to understand the responses of organisms to stimuli. Representing such data requires an integrated view of experimental and informatic protocols. The proposed system, named BioSig, provides the foundation for cataloging cellular responses as a function of specific conditioning, treatment, staining, etc., for either fixed tissue or living cell studies. A data model has been developed to capture experimental variables and map them to image collections and their computed representation. This representation is hierarchical and spans across sample tissues, cells, and organelles, which are imaged with light microscopy. At each layer, content is represented with an attributed graph, which contains information about cellular morphology, protein localization, and cellular organization in tissue or cell culture. The Web-based multilayer informatics architecture uses the data model to provide guided workflow access for content exploration.

Multiscale iterative voting for differential analysis of stress response for 2D and 3D cell culture models.

Three‐dimensional (2D) cell culture models have emerged as the basis for improved cell systems biology. However, there is a gap in robust computational techniques for segmentation of these model systems that are imaged through confocal or deconvolution microscopy. The main issues are the volume of data, overlapping subcellular compartments and variation in scale or size of subcompartments of interest, which lead to ambiguities for quantitative analysis on a cell‐by‐cell basis. We address these ambiguities through a series of geometric operations that constrain the problem through iterative voting and decomposition strategies. The main contributions of this paper are to (i) extend the previously developed 2D radial voting to an efficient 3D implementation, (ii) demonstrate application of iterative radial voting at multiple subcellular and molecular scales, and (iii) investigate application of the proposed technology to two endpoints between 2D and 3D cell culture models. These endpoints correspond to kinetics of DNA damage repair as measured by phosphorylation of γH2AX, and the loss of the membrane‐bound E‐cadherin protein as a result of ionizing radiation.

Feature based visualization of geophysical data

Our goal is to develop a feature based framework for data mining and forecasting from geophysical data fields. These data may be generated from either numerical simulation models or space based platforms. This paper focuses on pertinent features from sea surface temperature (SST) fields that are observed with the AVHRR satellite. Our contribution consist of three components: (1) A method for tracking feature velocities from from fluid motion with incompressibility constraint, (2) a method for localizing singular events such as vortices and saddle points from underlying feature velocities, and (3) application of our protocol to 12 years of high resolution real data to reveal novel seasonal and inter-annual trends based on computed events.

Harmonic cut and regularized centroid transform for localization of subcellular structures

Two novel computational techniques, harmonic cut and regularized centroid transform, are developed for segmentation of cells and their corresponding substructures observed with an epi-fluorescence microscope. Harmonic cut detects small regions that correspond to subcellular structures. These regions also affect the accuracy of the overall segmentation. They are detected, removed, and interpolated to ensure continuity within each region. We show that interpolation within each region (subcellular compartment) is equivalent to solving the Laplace equation on a multi-connected domain with irregular boundaries. The second technique, referred to as the regularized centroid transform, aims to separate touching compartments. This is achieved by adopting a quadratic model for the shape of the object and relaxing it for final segmentation.

CHEF: convex hull of elliptic features for 3D blob detection

We present an efficient protocol for robust detection of 3D blobs from volumetric datasets. The approach has three steps. The first step of the process detects elliptic features by classifying the Hessian of the scale space representation of the volume data. These features are then grouped into 3D connected components, which are subsequently partitioned by computing a convex hull of each connected component. The proposed framework was applied to a database of multicellular systems for detailed quantitative analysis.

Detection of vortices and saddle points in SST data

We extend the Horn-Schunck model of flow field computation to incorporate incompressibility for tracking fluid motion. This is expressed as a weak form of zero-divergence constraint in the variational problem and implemented with a multigrid approach for efficient computation. The resulting feature displacement velocity field provides the basis for higher level abstraction and representation of the data for data mining. A robust and efficient algorithm, based on the Jordan curve index, for detecting vortices and saddle points in feature displacement fields derived from sequences of satellite-derived SST fields is presented.

Singular features in sea surface temperature data

We propose to detect singular-features in order to generate an intelligent summary of high resolution spatio-temporal data that are obtained from satellite-based observations of the ocean. Toward this objective, we extend the Horn-Schunck model of flow field computation to incorporate incompressibility for tracking fluid motion. This is expressed as a zero-divergence constraint in the variational problem and an efficient multigrid implementation of it is introduced. Additionally, we show an effective localization of event features, such as vortices and saddle points, in the velocity field that can be used for subsequent abstraction, query and statistical analysis.

SEGMENTATION OF MAMMOSPHERE STRUCTURES FROM VOLUMETRIC DATA

3D cell culture assays have emerged as the basis of an improved model system for evaluating therapeutic agents, molecular probes, and exogenous stimuli. However, there is a gap in robust computational techniques for segmentation of image data that are collected through confocal or deconvolution microscopy. The main issue is the volume of data, overlapping subcellular compartments, and variation in scale and size of subcompartments of interest. A geometric technique has been developed to bound the solution of the problem by first localizing centers of mass for each cell and then partitioning clumps of cells along minimal intersecting surfaces. An approximate solution to the center of mass is realized through iterative spatial voting, which is tolerant to variation in shape morphologies and overlapping compartments and is shown to have an excellent noise immunity. These approximate estimates to centers of mass are then used to partition a clump of cells along minimal intersecting surfaces that are estimated by Radon transform. Examples on real data and performance of the system over a large population of data are evaluated. Furthermore, it is shown that the proposed methodology is extensible in terms of its application to protein localization studies