Digital holographic microscopy can quantitatively image the biological samples label-free and noninvasively. It is key to extract the +1-term spectrum from the hologram spectrum, which is crucial to the quality of the reconstructed image. Therefore, an adaptive spatial filtering method based on fuzzy C-means and phase spectrum of a hologram is proposed to extract the +1-term spectrum without any prior knowledge. The maximum phase value point of phase spectrum is found, which must be located in the +1-term spectrum. Then, this point is first introduced to locate the +1-term spectrum region. Two classifications and three regions (+1-term, −1-term, and zero-order term spectra regions) are obtained by fuzzy C-means in the amplitude spectrum. Subsequently, the minimum distance between the centroids of the three regions and the maximum phase point is used to judge the +1-term spectrum region. Finally, a filtering window is obtained by the edge of the +1-term spectrum region and the +1-term spectrum is adaptively extracted. Compared to other spatial filtering methods, the proposed method avoids dependence on a prior custom mask and suppresses the higher frequency noise. Most importantly, the experimental results on a number of human cells and a phase step demonstrate the feasibility and efficiency of the proposed method.
In order to improve the accuracy and efficiency of weld defect segmentation in automatic radiographic nondestructive
testing and evaluation(NDT&E), an effective weld defect segmentation algorithm based on flooding has been developed,
which has the self-adaptive characteristics. Firstly, the defect’s feature points are extracted from the scale space of
radiographic films. Based on the information of defect points, the seed points and seed domains of defect discrimination
are adaptively determined, in which the defect segmentation seed will be searched. Then, aiming at the sparsity of weld
defects and canyon characteristics of 3D topographic map of defect regions, the drip-watering and water flooding have
been used for reference. The flooding is carried out by using line-flooding algorithm, in which water starts from defect
seed points and flows to the neighbor regions in order. On the basis of the flooding-area change and flooding-level
ascending velocity, the defect segmentation threshold values are determined and the weld defects also are segmented
from the radiographic films. At last, the comparative experiments have been carried out to compare the proposed
algorithm with the watershed segmentation algorithm and background subtraction segmentation algorithm. And the
experiment results confirm that the proposed algorithm obviously improves the accuracy and efficiency of weld defect’s
segmentation.
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