Quadratic blind linear unmixing: A graphical user interface for tissue characterization.
Quadratic blind linear unmixing: A graphical user interface for tissue characterization.
复制标题
DOI：
10.1016/j.cmpb.2015.10.016
复制
发表时间：
2016-02
影响因子：
6.1
通讯作者：
Jo JA
中科院分区：
文献类型：
--
作者：
Gutierrez-Navarro O;Campos-Delgado DU;Arce-Santana ER;Jo JA
光谱分解是将样本数据分解为基本成分及其丰度的过程。以前的工作一直集中在盲解混合的多光谱荧光寿命成像显微镜（m-FLIM）数据集下的线性混合模型和二次近似。该方法提供了快速的线性分解，并且可以在没有最大组件或端元数量限制的情况下工作。因此，这项工作提出了一个交互式软件，实现我们的盲端元和丰度提取（BEAE）和二次盲线性解混（QBLU）算法在Matlab中。我们提出的软件的选项和功能进行了详细描述。当组分数目已知时，我们的软件可以估计组成端元及其丰度。当没有先验知识时，该软件可以提供一个完全盲解来估计组分的数量、端元及其丰度。三个案例研究的表征验证了新软件的性能：离体人冠状动脉，人乳腺癌细胞样本，和体内仓鼠口腔粘膜。该软件可在一个开发机构的托管网页上免费提供，使用户能够快速、易于使用和高效地进行多光谱/超光谱数据分解。
Spectral unmixing is the process of breaking down data from a sample into its basic components and their abundances. Previous work has been focused on blind unmixing of multi-spectral fluorescence lifetime imaging microscopy (m-FLIM) datasets under a linear mixture model and quadratic approximations. This method provides a fast linear decomposition and can work without a limitation in the maximum number of components or end-members. Hence this work presents an interactive software which implements our blind end-member and abundance extraction (BEAE) and quadratic blind linear unmixing (QBLU) algorithms in Matlab. The options and capabilities of our proposed software are described in detail. When the number of components is known, our software can estimate the constitutive end-members and their abundances. When no prior knowledge is available, the software can provide a completely blind solution to estimate the number of components, the end-members and their abundances. The characterization of three case studies validates the performance of the new software: ex-vivo human coronary arteries, human breast cancer cell samples, and in-vivo hamster oral mucosa. The software is freely available in a hosted webpage by one of the developing institutions, and allows the user a quick, easy-to-use and efficient tool for multi/hyper-spectral data decomposition.