Effective quantum mechanics–embedded nanoparticle occlusion analysis framework
Source abstract
Abstract
While the occlusion analysis of nanoparticles (NPs) has been important in detecting NPs in images, most existing methods fail to accurately segment contours, and additional time-consuming manual inspections are required. To overcome this issue, a new and effective quantum-mechanics-embedded NP occlusion analysis framework is proposed. The integration of quantum mechanics in occlusion analysis helps identify irregular shaped NPs with noise. Brownian motion–based drift and diffusion were used to detect whether the NPs had occluded regions. The decision parameters and relevant quantum mechanics–based differential equations were derived from scanning electron microscopy images of the applied NPs. The overall proposed framework consists of five stages, from preprocessing to quantum mechanics–based contour estimation. To demonstrate the effectiveness of the proposed framework, comparative analyses with the existing methods were carried out. This revealed that the proposed framework contributed to more accurate occlusion identification.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Cell Image Analysis Techniques · Image Processing Techniques and Applications · Photoacoustic and Ultrasonic Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (43)
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