Featured Application This paper describes the application of linear unmixing to classify
intraoperative hyperspectral images of in vivo brain tissue with a reduced computational cost …

In some applications of biomedical imaging, a linear mixture model can represent the
constitutive elements (end-members) and their contributions (abundances) per pixel of the …

Red blood cell counting, as a routine examination, plays an important role in medical
diagnoses. Although automated hematology analyzers are widely used, manual microscopic …

Fluorescence lifetime microscopy imaging (FLIM) is an optic technique that allows a
quantitative characterization of the fluorescent components of a sample. However, for an …

Noninvasive methods for assessing in-vivo skin blood perfusion parameters, such as
hemoglobin oxygenation, are crucial for diagnosing and monitoring microvascular diseases …

Plant fluorescence is too susceptible to interference and unsuitable for plant remote sensing
monitor. A plant fluorescence lifetime imaging technology based on plant remote sensing …

Time-deconvolution of the instrument response from fluorescence lifetime imaging
microscopy (FLIM) data is usually necessary for accurate fluorescence lifetime estimation. In …

In this paper, we investigate novel low-dimensional and model-free representations for multi-
spectral fluorescence lifetime imaging microscopy (m-FLIM) data. We depart from the …

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 …

The deconvolution process is a key step for quantitative evaluation of fluorescence lifetime
imaging microscopy (FLIM) samples. By this process, the fluorescence impulse responses …