Two different phase-estimation methods that have been developed for the computation of the optical path-length (OPL) distribution of a specimen from DIC images are compared in this paper. The first method is based on ...
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(纸本)0819427004
Two different phase-estimation methods that have been developed for the computation of the optical path-length (OPL) distribution of a specimen from DIC images are compared in this paper. The first method is based on the Wiener filter approach and uses only a single DIC image for the determination of the OPL distribution. The second phase-estimation method is based on the conjugate-gradient optimization method and estimates the OPL distribution using rotational-diversity DIC images;i.e. multiple DIC images obtained by rotating the specimen. For this study, 24 different DIC images of a single bovine spermatozoa head were acquired by rotating the cell by approximately 15 degrees between images. The images were registered and aligned using fiducial marks, and then processed with both methods. Results obtained with the filtering method were found to be dependent on the orientation of the cell with respect to the shear direction. Comparison of the integrated optical path length (IOPL) computed with the the filtering method and the rotational-diversity method using two, four and eight DIC images at different rotation angles showed that the IOPL estimated with the rotational-diversity method is less dependent on the rotation angle, even when only two images separated by 90-degree cell rotation are used for the phase estimation. Our results show that the use of rotational-diversity images in the determination of the OPL distribution is very beneficial because it overcomes the directional dependence of DIC imaging.
The automatic reconstruction of 3-D structures from stacks of 2-D images is an important problem in medical image analysis. In neuroscience, in particular, the availability of explicit 3-D models of the dendritic tree...
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The automatic reconstruction of 3-D structures from stacks of 2-D images is an important problem in medical image analysis. In neuroscience, in particular, the availability of explicit 3-D models of the dendritic tree structure of a neuron can be a valuable tool for understanding the complexity of neuronal function and neuronal morphology. In this work, the dendritic tree structure is imaged at different levels through the tissue using a laser scanning confocal microscope. The aim of the software is to deliver an explicit representation of the tree as a generalised cylinder model. Automatic techniques often fail to produce a continuous 3-D model and manual or semi-automatic techniques can be particularly labour intensive. In this paper we describe algorithms which lead to a continuous 3-D cylinder model representation of the dendritic tree with a minimum of user involvement. The initial stage of the approach involves the identification of voxels with a high probability of being on the centre-lines of the dendritic structure. These points are linked to form a skeleton using cost minimisation techniques. In the final stage the full 3-D structure is extracted around the centre-lines and represented by generalised cylinders. This paper provides details of the algorithms in each of the stages together with some of the results of using the method on both synthetic and real data sets.
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