Benjamin Xueqi Guan

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This paper proposes 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(More)
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(More)
AbstractBlebbing is an important biological indicator in determining the health of human embryonic stem cells hESC. Especially, areas of a bleb sequence in a video are often used to distinguish two cell blebbing behaviors in hESC: dynamic and apoptotic blebbings. This paper analyzes various segmentation methods for bleb extraction in hESC videos and(More)
In this paper, 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(More)
This paper proposes a bio-driven algorithm that detects cell regions automatically in the human embryonic stem cell (hESC) images obtained using a phase contrast microscope. The algorithm uses both statistical intensity distributions of foreground/hESCs and background/substrate as well as cell property for cell region detection. The intensity distributions(More)
Determining the meaningful texture features for human embryonic stem cells (hESC) is important in the development of online hESC classification system. This paper proposes the use of novel support vector machine with bio-inspired one-against-all (OAA) multi-class structural and statistical Gabor descriptors for hESC classification. It investigates the(More)
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(More)
Human Embryonic Stem Cells (HESCs) have an important role in the futuristic medicine. A regenerative medicine with stem cell can be used to treat various diseases such as cancer, Parkinson’s disease, Huntington’s disease, Type 1 diabetes mellitus etc. Biologists are interested in the number of non-dynamic blebbing single unattached HESCs (NDBSU-HESCs) in(More)
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