Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiplicative Updates for Nonnegative Quadratic Programming
Neural Computation
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonnegative Mixed-Norm Preconditioning for Microscopy Image Segmentation
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Cell segmentation, tracking, and mitosis detection using temporal context
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Restoring DIC microscopy images from multiple shear directions
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Pipeline for tracking neural progenitor cells
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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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.