Understanding the Optics to Aid Microscopy Image Segmentation
Image segmentation is essential for many automated microscopy image analysis systems. Rather than treating microscopy images as general natural images and rushing into the image processing warehouse for solutions, we propose to study a microscope's optical properties to model its image formation process first using phase contrast microscopy as an exemplar. It turns out that the phase contrast imaging system can be relatively well explained by a linear imaging model. Using this model, we formulate a quadratic optimization function with sparseness and smoothness regularizations to restore the "authentic" phase contrast images that directly correspond to specimen's optical path length without phase contrast artifacts such as halo and shade-off. With artifacts removed, high quality segmentation can be achieved by simply thresholding the restored images. The imaging model and restoration method are quantitatively evaluated on two sequences with thousands of cells captured over several days.
Z. Yin et al., "Understanding the Optics to Aid Microscopy Image Segmentation," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6361 LNCS, no. 1, pp. 209-217, Springer Verlag, Sep 2010.
The definitive version is available at https://doi.org/10.1007/978-3-642-15705-9_26
13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 (2010: Sep. 20-24, Beijing, China)
Keywords and Phrases
Automated Microscopy; High-Quality Segmentation; Image Formation Process; Imaging Model; Linear Imaging; Microscopy Images; Natural Images; Optical Path Lengths; Phase Contrasts; Phase-Contrast Image; Phase-Contrast Imaging; Phase-Contrast Microscopy; Quadratic Optimization; Restoration Methods; Thresholding; Digital Image Storage; Imaging Systems; Medical Computing; Medical Imaging; Optical Data Processing; Optical Properties; Restoration; Image Segmentation
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