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Intel Labs Pittsburgh

By Zhaozheng Yin, Kang Li, Takeo Kanade and Mei Chen


Abstract. 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 image that directly corresponds 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 image. The imaging model and restoration method are quantitatively evaluated on two sequences with thousands of cells captured over several days.

Year: 2011
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