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年龄相关性黄斑变性的体内快照高光谱图像分析

In vivo snapshot hyperspectral image analysis of age-related macular degeneration.

作者信息

Lee N, Wielaard J, Fawzi A A, Sajda P, Laine A F, Martin G, Humayun M S, Smith R T

机构信息

Heffner Biomedical Imaging Laboratory (HBIL) and the Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5363-6. doi: 10.1109/IEMBS.2010.5626463.

Abstract

Drusen, the hallmark lesions of age related macular degeneration (AMD), are biochemically heterogeneous and the identification of their biochemical distribution is key to the understanding of AMD. Yet the challenges are to develop imaging technology and analytics, which respect the physical generation of the hyperspectral signal in the presence of noise, artifacts, and multiple mixed sources while maximally exploiting the full data dimensionality to uncover clinically relevant spectral signatures. This paper reports on the statistical analysis of hyperspectral signatures of drusen and anatomical regions of interest using snapshot hyperspectral imaging and non-negative matrix factorization (NMF). We propose physical meaningful priors as initialization schemes to NMF for finding low-rank decompositions that capture the underlying physiology of drusen and the macular pigment. Preliminary results show that snapshot hyperspectral imaging in combination with NMF is able to detect biochemically meaningful components of drusen and the macular pigment. To our knowledge, this is the first reported demonstration in vivo of the separate absorbance peaks for lutein and zeaxanthin in macular pigment.

摘要

玻璃膜疣是年龄相关性黄斑变性(AMD)的标志性病变,其在生化方面具有异质性,确定其生化分布是理解AMD的关键。然而,挑战在于开发成像技术和分析方法,既要在存在噪声、伪影和多个混合源的情况下考虑高光谱信号的物理生成,又要最大限度地利用完整的数据维度来揭示临床相关的光谱特征。本文报道了使用快照高光谱成像和非负矩阵分解(NMF)对玻璃膜疣和感兴趣的解剖区域的高光谱特征进行的统计分析。我们提出将具有物理意义的先验作为NMF的初始化方案,以找到能够捕捉玻璃膜疣和黄斑色素潜在生理特征的低秩分解。初步结果表明,快照高光谱成像与NMF相结合能够检测出玻璃膜疣和黄斑色素具有生化意义的成分。据我们所知,这是首次在体内报道黄斑色素中叶黄素和玉米黄质的单独吸收峰。

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Convex and semi-nonnegative matrix factorizations.凸和半非负矩阵分解。
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Coupled two-way clustering analysis of gene microarray data.基因芯片数据的耦合双向聚类分析
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