Differentiation of normal skin and melanoma using high resolution hyperspectral imaging
Differentiation of normal skin and melanoma using high resolution hyperspectral imaging
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DOI：
10.4161/cbt.5.8.3261
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发表时间：
2006-08-01
影响因子：
3.6
通讯作者：
El-Deiry, Wafik S.
中科院分区：
文献类型：
--
作者：
Dicker, David T.;Lerner, Jeremy;El-Deiry, Wafik S.
我们研究了使用高分辨率高光谱成像显微镜，使用正常和异常皮肤、良性痣和黑色素瘤的苏木精伊红染色制剂来检测皮肤组织的异常情况。这项研究的目标是提供可供任何研究人员使用的客观数据；并形成参考光谱数据库的开端。所有光谱特征均以透射率和吸收率的形式获得，连续波长采集范围为 400 至 800 nm；光谱分辨率接近 1 nm。活检切片以不同的样品厚度、染色和放大倍率进行表征，以确定它们对光谱表征的影响。使用光谱波形互相关分析（一种线性不变的算法）对光谱进行分类。分类光谱纳入光谱库；从视野中获取的所有光谱都与库光谱相关联，以达到用户确定的量化置信阈值（最小相关系数）。结果表明，只要控制染色和切片厚度，我们初始数据集中的所有皮肤状况都可以客观区分。我们还证明，可以创建一个包含生物信息学和聚类分析的参考光谱库数据库。这将有助于多个实验室参与目标光谱信息的输入和检索。
We investigated the use of high resolution hyperspectral imaging microscopy to detect abnormalities in skin tissue using hematoxylin eosin stained preparations of normal and abnormal skin, benign nevi and melanomas. A goal of this study was to provide objective data that could be utilized by any researcher; and form the beginnings of a reference spectral data base. All spectral characterizations were acquired in percent transmission, and absorption, with contiguous wavelength acquisition between 400 and 800 nm; and a spectral resolution of similar to 1 nm. Biopsy sections were characterized with varying sample thickness, staining and magnification in order to determine their impact on spectral characterizations. Spectra were classified using spectral waveform cross correlation analysis, an algorithm that is linearity invariant. Classified spectra were incorporated into spectral libraries; and all spectra acquired from the field of view were correlated with library spectra to a quantified, user determined, confidence threshold (minimum correlation coefficient). The results revealed that all skin conditions in our initial data sets could be objectively differentiated providing that staining and section thickness was controlled. We also demonstrated that it is likely that a reference spectral library database could be created to include bioinformatics and cluster analysis. This would assist multiple laboratories to participate in the input and retrieval of target spectral information.