Phase Contrast Image Restoration Via Dictionary Representation of Diffraction Patterns
The restoration of microscopy images makes the segmentation and detection of cells easier and more reliable, which facilitates automated cell tracking and cell behavior analysis. In this paper, the authors analyze the image formation process of phase contrast images and propose an image restoration method based on the dictionary representation of diffraction patterns. By formulating and solving a min-ℓ1 optimization problem, each pixel is restored into a feature vector corresponding to the dictionary representation. Cells in the images are then segmented by the feature vector clustering. In addition to segmentation, since the feature vectors capture the information on the phase retardation caused by cells, they can be used for cell stage classification between intermitotic and mitotic/apoptotic stages. Experiments on three image sequences demonstrate that the dictionary-based restoration method can restore phase contrast images containing cells with different optical natures and provide promising results on cell stage classification.
H. Su et al., "Phase Contrast Image Restoration Via Dictionary Representation of Diffraction Patterns," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7512 LNCS, pp. 615-622, Springer Verlag, Oct 2012.
The definitive version is available at https://doi.org/10.1007/978-3-642-33454-2_76
Keywords and Phrases
Cells; Classification (of Information); Cytology; Diffraction; Diffraction Patterns; Image Reconstruction; Image Segmentation; Interferometry; Medical Computing; Medical Imaging; Optimization; Restoration; Feature Vectors; Image Formation Process; Image Sequence; Microscopy Images; Optimization Problems; Phase Retardation; Phase-contrast Image; Restoration Methods; Image Analysis; Algorithm; Article; Automated Pattern Recognition; Computer Assisted Diagnosis; Image Enhancement; Information Retrieval; Methodology; Phase Contrast Microscopy; Refractometry; Reproducibility; Sensitivity and Specificity, Algorithms; Image Enhancement; Image Interpretation; Computer-assisted; Information Storage and Retrieval; Microscopy; Phase-Contrast; Pattern Recognition; Automated; Refractometry; Reproducibility of Results; Sensitivity and Specificity
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Article - Conference proceedings
© 2012 Springer Verlag, All rights reserved.
01 Oct 2012