Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Plant Cells, Cancer Cells

Temporal dynamics of tip fluorescence predict cell growth behavior in pollen tubes

Proposed is a method of extracting features from the tip fluorescence signal which were used to distinguishing between straight vs. turning growth behavior. The tip signal was obtained as a ratio of the average membrane-to-cytoplasm fluorescence values over time. A two-stage scheme was used to automatically detect individual growth intervals/cycles from the tip signal and split the experimental video into growth segments. In each growth segment, relevant features were extracted. An initial classification used structure-based features to distinguish between straight vs. turning growth cycles. The signal-based features were then used to train a Naive Bayes classifier to refine the misclassifications of the initial classification.

Modeling and classifying tip dynamics of growing cells in video

Plant biologists study pollen tubes to discover the functions of many proteins/ions and map the complex network of pathways that lead to an observable growth behavior. Many growth models have been proposed that addressed parts of the growth process: internal dynamics and cell wall dynamics, but they did not distinguish between the two types of growth segments: straight versus turning behavior. We proposed a method of classifying segments of experimental videos by extracting features from the growth process during each interval. We used a stress–strain relationship to measure the extensibility in the tip region. A biologically relevant three-component Gaussian was used to model spatial distribution of tip extensibility and a second-order damping system was used to explain the temporal dynamics. Feature-based classification showed that the location of maximum tip extensibility was the most distinguishing feature between straight versus turning behavior

Segmentation of pollen tube growth videos using dynamic bi-modal fusion and seam carving

A new automated technique is presented for boundary detection by fusing fluorescence and brightfield images, and a new efficient method of obtaining the final cell boundary through the process of Seam Carving is proposed. This approach took advantage of the nature of the fusion process and also the shape of the pollen tube to efficiently search for the optimal cell boundary. In video segmentation, the first two frames were used to initialize the segmentation process by creating a search space based on a parametric model of the cell shape. Updates to the search space were performed based on the location of past segmentations and a prediction of the next segmentation.

Dynamic Bi-modal fusion of images for segmentation of pollen tubes in video

Biologists studied pollen tube growth to understand how internal cell dynamics affected observable structural characteristics like cell diameter, length, and growth rate. Fluorescence microscopy was used to study the dynamics of internal proteins and ions, but this often produced images with missing parts of the pollen tube. Brightfield microscopy provided a low-cost way of obtaining structural information about the pollen tube, but the images were crowded with false edges. We proposed a dynamic segmentation fusion scheme that used both Bright-Field and Fluorescence images of growing pollen tubes to get a unified segmentation. Knowledge of the image formation process was used to create an initial estimate of the location of the cell boundary. Fusing this estimate with an edge indicator function amplified desired edges and attenuated undesired edges. The cell boundary was obtained using Level Set evolution on the fused edge indicator function.

Understanding pollen tube growth dynamics using the Unscented Kalman Filter

Knowledge of the dynamics of pollen tube growth will provide a basis for understanding more complex cells that exhibit similar growth behavior. Current pollen tube growth models are a collection of differential equations that represent the level of understanding that biologists have concerning apical growth. Due to their complex nature, these models are not used to verify observed behavior in living cells as seen under a microscope. We propose biologically relevant functions based on knowledge of the growth process to explain the dynamics of model parameters.

Background suppressing Gabor energy filtering

In the field of facial emotion recognition, early research advanced with the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization capability, and the compactness of representation. In this paper, we propose a novel texture feature inspired by Gabor energy filters, called background suppressing Gabor energy filtering. The feature has a generalization component that removes background texture. It has a reduced feature vector size due to maximal representation and soft orientation histograms, and it is a white box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the development set. We also demonstrate applicability of our approach beyond facial emotion recognition which yields improved classification rate over the Gabor filter for four bioimaging datasets by an average of 8.22%.

Integrated Model for Understanding Pollen Tube Growth in Video

Pollen tube growth is an essential part of the sexual reproductive process in plants. It is the result of a complex interaction of cytoplasmic contents (proteins, ions, cellular structures, etc.). Existing pollen tube models use differential equations to represent these complex intra-cellular interactions that lead to growth. As a result of this complex nature, these models are not used to verify the shape and growth behavior observed in living cells. We present a method of analyzing the growth behavior of pollen tubes in experimental videos through affine transformations on the detected cell tip. The method relies on underlying biological knowledge about the growth process and leverages these processes to determine tip morphology. Experimental results on videos of growing pollen tube cells show that our method is superior to the current method of treating cell tip morphology as well as adaptive active appearance models.

Automated Spatial Analysis of ARK2: Putative Link Between Microtubules and Cell Polarity

In leaves of A. thaliana, there exists an intricate network of epidermal surface layer cells responsible for anatomical stability and vigor of flexibility to the entire leaf. Rho GTPases direct this organization of cell polarity, but full understanding of the underlying mechanisms demands further inquiry. We conduct two experiments: (1) a novel procedure is proposed that could be used in other life and plant science studies to quantify microtubule orientation, and (2) shape analysis. We hypothesize ARK2 as a putative interactor in cell polarity maintenance through stabilization of microtubule ordering. We are the first to automate pavement cell phenotype analysis for cell polarity and microtubule orientation. Breakthroughs in the signaling network regulating leaf cell polarity and development will lead science into the frontier of genetically modifying leaves to dramatically increase Earth's plant biomass; impending food shortages in the 21st century will be well served by such research.

Spatiotemporal Dynamics of the Growth of Pollen Tubes Using GFP-tagged RIC4 Videos

ROP1, a Rho family GTPase enzyme, activates the downstream target RIC4. RIC4 is an accurate reporter of ROP1 activity, which is periodically localized at the apex of the plasma membrane in pollen tubes. It allows the positive feedback relationship between pollen tube growth and ROP1 activity to be established based on its observed behavior. It regulates the growth of filamentous actin that directly affects pollen tube growth. However, the displacement of the plasma membrane and frequency of oscillation of the localization of RIC4 at the tip have not been quantified. Most current studies of pollen tubes are done by analyzing the limited amount of data by hand. As a result, pollen tube growth patterns are still not thoroughly understood. The proposed research develops computer algorithms to analyze laser microscopy videos of pollen tubes with GFP-tagged RIC4.

Segmentation of Images Having Unimodal Distribution

A gradient relaxation method based on maximizing a criterion function was studied and compared to the nonlinear probabilistic relaxation method for the purpose of segmentation of images having unimodal distributions. Although both methods provided comparable segmentation results, the gradient method had the additional advantage of providing control over the relaxation process by choosing three parameters which could be tuned to obtain the desired segmentation results at a faster rate.