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