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

 

 

Stem Cells

Extraction of Blebs in Human Embryonic Stem Cell Videos

Analyzed are various segmentation methods for bleb extraction in hESC videos which introduces a bio-inspired score function to improve the performance in bleb extraction. Full bleb formation consists of bleb expansion and retraction. Blebs change their size and image properties dynamically in both processes and between frames. Therefore, adaptive parameters are needed for each segmentation method. A score function derived from the change of bleb area and orientation between consecutive frames is proposed which provides adaptive parameters for bleb extraction in videos.

Evaluating cell processes, quality, and biomarkers in pluripotent stem cells using video bioinformatics

Presented is an unbiased and automated high-content profiling toolkit, StemCellQC, which non-invasively extracts information on cell quality and cellular processes from time-lapse phase-contrast videos. Twenty four morphological and dynamic features were analyzed in healthy, unhealthy, and dying human embryonic stem cell (hESC) colonies to identify those features that were affected in each group. StemCellQC distinguished healthy and unhealthy/dying hESC colonies with 96% accuracy by non-invasively measuring and tracking dynamic and morphological features over 48 hours. Changes in cellular processes were monitored by StemCellQC and predictions could be made about the quality of pluripotent stem cell colonies. This toolkit reduced the time and resources required to track multiple pluripotent stem cell colonies and eliminated handling errors and false classifications due to human bias.

Bio-Driven Cell Region Detection in Human Embryonic Stem Cell Assay

Resulting image after noise filtering We present a bio-driven algorithm that detects cell regions automatically in the human embryonic stem cell (hESC) images obtained using a phase contrast microscope. The intensity distributions of foreground/hESCs and background/substrate are modelled as a mixture of two Gaussians. In comparison with the state-of-the-art methods, the proposed method is able to detect the entire cell region instead of fragmented cell regions. It also yields high marks on measures such as Jaccard similarity, Dice coefficient, sensitivity and specificity.

Comparison of Texture Features for Human Embryonic Stem Cells with Bio-Inspired Multi-Class Support Vector Machine

Determining the meaningful texture features for human embryonic stem cells (hESC) is important in the development of an online hESC classification system. We propose the use of a novel support vector machine with bio-inspired one-against-all (OAA) multi-class structural and statistical Gabor descriptors for hESC classification. We investigated the statistical histogram information at four different orientations and two different window sizes of the Gabor filter. We've also demonstrated that statistical Gabor features are more accurate and reliable than conventional historgram based features.

Automatic Cell Region Detection by K-means with Weighted Entropy

We propose an automatic method to detect human embryonic stem cell regions. The proposed method utilizes the K-means algorithm with weighted entropy. As in phase contrast images the cell regions have high intensity variation, they usually yield higher entropy values than the substrate regions which have less intensity variation. Thus, the entropy can be used as an important feature for the detection of stem cells. However, homogeneity in intensity within some of the cell bodies and halos surrounding the cell bodies also gives low entropy values. Therefore, we introduce a weighted entropy formulation which fuses entropy and image intensity information to detect the entire cell regions.

Automated Human Embryonic Stem Cell Detection

We present an automated detection method with simple algorithm for detecting human embryonic stem cell (hESC) regions in phase contrast images. The algorithm uses both the spatial information as well as the intensity distribution for cell region detection. The method is modeled as a mixture of two Gaussians; hESC and substrate regions. The paper validates the method with various videos acquired under different microscope objectives.

Detection of Non-dynamic Blebbing Single Unattached Human Embryonic Stem Cells

Human Embryonic Stem Cells (HESCs) are promising for the treatment of many diseases and for toxicological testing. There is a great interest among biologists to automatically determine the number of various types of cells in a population of mixed morphologies. This study addresses quantification of non-dynamic blebbing single unattached human embryonic stem cells (NDBSU-HESCs) that are in suspension and do not show evidence of blebbing. Current image processing methods are inadequate for detecting these cells in real time. We propose a method for NDBSU-HESC detection by using multiple trained classifiers, where each classifier eliminates cells with properties unmatched to NDBSU-HESCs. The paper validates the method with many videos captured with live stem cells.

Human Embryonic Stem Cell Detection by Spatial Information and Mixture of Gaussians

Human Embryonic Stem Cells (HESCs) possess the potential to provide treatments for cancer, Parkinson's disease, Huntington's disease, Type 1 diabetes, mellitus, etc. Consequently, HESCs are often used in the biological assay to study the effects of chemical agents in the human body. However, detection of HESC is often a challenge in phase contrast images. To improve the accuracy of HESC colony detection, we combine spatial information and the outcome of a mixture of Gaussians model. While a mixture of Gaussians generates reasonable labels for various regions of HESC images, it lacks spatial details and connectivity. Sets of spatially consistent candidate labeling are generated by median filtering the image at different scales followed by thresholding. An optimal combination of filter scale and threshold which maximizes the correlation coefficient between the spatial information and the mixture of Gaussians output is obtained. The paper validates the method for various HESC videos.

Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth

Mining information from video material is difficult to do without the aid of computer software. In this article, we introduce a video bioinformatics method for quantifying the growth of human embryonic stem cells (hESC) by analyzing time-lapse videos. To determine the rate of growth of these colonies, three CL-Quant recipes were developed which enables users to extract various types of data from video images. The first segmented the image into the colony and background, the second enhanced the image to define colonies throughout the video sequence accurately, and the third measured the number of pixels in the colony overtime. When the data obtained using the CL-Quant recipes and Photoshop were compared, results were virtually identical, indicating the CL-Quant recipes were truthful. The method described here could be applied to any video data to measure growth rates of hESC or other cells that grow in colonies.