A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Nonnegative Mixed-Norm Preconditioning for Microscopy Image Segmentation
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Understanding the optics to aid microscopy image segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
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Differential Interference Contrast (DIC) microscopy is a non-destructive imaging modality that has been widely used by biologists to capture microscopy images of live biological specimens. However, as a qualitative technique, DIC microscopy records specimen's physical properties in an indirect way by mapping the gradient of specimen's optical path length (OPL) into the image intensity. In this paper, we propose to restore DIC microscopy images by quantitatively estimating specimen's OPL from a collection of DIC images captured from multiple shear directions. We acquire the DIC images by rotating the specimen dish on the microscope stage and design an Iterative Closest Point algorithm to register the images. The shear directions of the image dataset are automatically estimated by our coarse-to-fine grid search algorithm. We develop a direct solver on a regularized quadratic cost function to restore DIC microscopy images. The restoration from multiple shear directions decreases the ambiguity among different individual restorations. The restored DIC images are directly proportional to specimen's physical measurements, which is very amenable for microscopy image analysis such as cell segmentation.