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单张高光谱图像从混浊介质中直接重建定性深度信息的原理验证。

Proof of Principle for Direct Reconstruction of Qualitative Depth Information from Turbid Media by a Single Hyper Spectral Image.

机构信息

Institute of Photonic Technologies (LPT), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Konrad-Zuse-Strasse 3/5, 91052 Erlangen, Germany.

Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordon-Strasse 6, 91052 Erlangen, Germany.

出版信息

Sensors (Basel). 2021 Apr 19;21(8):2860. doi: 10.3390/s21082860.

Abstract

In medical applications, hyper-spectral imaging is becoming more and more common. It has been shown to be more effective for classification and segmentation than normal RGB imaging because narrower wavelength bands are used, providing a higher contrast. However, until now, the fact that hyper-spectral images also contain information about the three-dimensional structure of turbid media has been neglected. In this study, it is shown that it is possible to derive information about the depth of inclusions in turbid phantoms from a single hyper-spectral image. Here, the depth information is encoded by a combination of scattering and absorption within the phantom. Although scatter-dominated regions increase the backscattering for deep vessels, absorption has the opposite effect. With this argumentation, it makes sense to assume that, under certain conditions, a wavelength is not influenced by the depth of the inclusion and acts as an iso-point. This iso-point could be used to easily derive information about the depth of an inclusion. In this study, it is shown that the iso-point exists in some cases. Moreover, it is shown that the iso-point can be used to obtain precise depth information.

摘要

在医学应用中,高光谱成象变得越来越普遍。它已被证明在分类和分割方面比普通的 RGB 成像更有效,因为使用了更窄的波长带,提供了更高的对比度。然而,直到现在,高光谱图像也包含关于混浊介质的三维结构的信息这一事实一直被忽视。在这项研究中,我们证明了从单个高光谱图像中可以提取混浊幻象中包含物的深度信息。在这里,深度信息通过幻象内的散射和吸收的组合进行编码。尽管散射占主导的区域增加了深血管的反向散射,但吸收则有相反的效果。根据这一论点,可以合理地假设,在某些条件下,波长不受包含物深度的影响,并且充当等照度点。这个等照度点可以用来方便地获取包含物深度的信息。在本研究中,证明了在某些情况下存在等照度点。此外,还表明可以使用等照度点来获得精确的深度信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d72/8073672/1c5ff323ec28/sensors-21-02860-g0A1.jpg

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