Automated Human Embryonic Stem Cell Detection

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 validates the method with various videos acquired under different microscope objectives.

[1]  Michael I. Jordan,et al.  On Convergence Properties of the EM Algorithm for Gaussian Mixtures , 1996, Neural Computation.

[2]  Sanjay Ranka,et al.  An effic ient k-means clustering algorithm , 1997 .

[3]  Sanjay Ranka,et al.  An Efficient Space-Partitioning Based Algorithm for the K-Means Clustering , 1999, PAKDD.

[4]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  J. Boone,et al.  The essential physics of medical imaging. Second edition. 2002 , 2003, European Journal of Nuclear Medicine and Molecular Imaging.

[6]  A. Murdoch,et al.  Derivation, growth and applications of human embryonic stem cells. , 2004, Reproduction.

[7]  Ninad Thakoor,et al.  A Statistical Approach for Intensity Loss Compensation of Confocal Microscopy Images , 2007, 2007 IEEE International Conference on Image Processing.

[8]  R Farnoush,et al.  Image Segmentation using Gaussian Mixture Model , 2008 .

[9]  R. Farnoosh,et al.  IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL , 2008 .

[10]  Suman Tatiraju.  Image Segmentation using k-means clustering , EM and Normalized Cuts , 2008 .

[11]  P. Talbot,et al.  Mouse and human embryonic stem cells: can they improve human health by preventing disease? , 2011, Current topics in medicinal chemistry.

[12]  J. Boone,et al.  Comprar Essential Physics Of Medical Imaging, International Edition | Jerrold T. Bushberg | 9781451118100 | Lippincott Williams & Wilkins , 2012 .