KEYWORDS: Video, Principal component analysis, Education and training, Image processing, Feature extraction, Data modeling, Video processing, Hough transforms, Edge detection, Visual process modeling
This study explored the relationship between the structure similarity coefficient extracted from time-lapse monitoring videos of embryos and the embryonic developmental process. It applied the K-means clustering method to analyze the clustering of structure similarity coefficients of different samples, and to investigate the correlation between the clusters with the ability of embryonic cells to develop into blastocysts. The proposed method started to work by using the Hough circle transform to detect cell contours and eliminate image impurities. It further investigated the correlation between the structure similarity coefficient calculated from the time-lapse imaging frames and the embryonic developmental process. In this study, the calculation of the structural similarity coefficient only considered the measure of structural contrast, which accurately reflected the disparity in gray distribution between two images. Normalization was employed to eliminate any influence from brightness and image contrast on the results. After considering the non-uniform distribution of statistical characteristics within an image, local windows were utilized to calculate both mean and variance. We found that the significant decline in the structure similarity coefficient curve corresponds to the event of cell division. The proposed method performed PCA dimensionality reduction on the structure similarity coefficients and applied the K-means clustering to analyze the clustering of sample data. Finally, it explored the relationship between the clustered groups and the ability of embryonic cells to develop into blastocysts. This study generated an effective predictive marker for morphological changes in embryonic cell development, contributing to the prediction of the developmental potential of embryonic cells.
A semi-blind image restoration approach is put forward in this paper for light-microscopy system. Microscope creates
unavoidable light artefacts because of the Point Spread Function (PSF) of the optical system, and our early research
shows that PSF of the microscopy system can be modeled as isotropic Gaussian blur. That is the motivation of this paper.
We present an algorithm, based on functional minimization, which integrates Canny edge detection with semi-blind
deconvolution. An alternating minimization (AM) implicit iterative scheme is devised to recover the image and
simultaneously identify PSF. Good performance is observed with numerically blurred images and really microscopic
images, even under the presence of high noise level.
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