Restoring DIC Microscopy Images from Multiple Shear Directions
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.
Z. Yin et al., "Restoring DIC Microscopy Images from Multiple Shear Directions," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6801 LNCS, pp. 384-397, Springer Verlag, Jul 2011.
The definitive version is available at https://doi.org/10.1007/978-3-642-22092-0_32
Keywords and Phrases
Biological Specimens; Cell Segmentation; Coarse-to-Fine; Differential Interference Contrast Microscopy; Direct Solvers; Grid-Search Algorithm; Image Datasets; Image Intensities; Imaging Modality; Iterative Closest Point Algorithm; Microscopy Images; Non Destructive; Optical Path Lengths; Physical Measurement; Quadratic Cost Functions; Shear Direction, Algorithms; Data Processing; Image Segmentation; Restoration; Medical Imaging
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01 Jul 2011