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Main Menu - Block
- Overview
- Anatomy and Histology
- Cryo-Electron Microscopy
- Electron Microscopy
- Flow Cytometry
- Gene Targeting and Transgenics
- High Performance Computing
- Immortalized Cell Line Culture
- Integrative Imaging
- Invertebrate Shared Resource
- Janelia Experimental Technology
- Mass Spectrometry
- Media Prep
- Molecular Genomics
- Stem Cell & Primary Culture
- Project Pipeline Support
- Project Technical Resources
- Quantitative Genomics
- Scientific Computing
- Viral Tools
- Vivarium
Abstract
Spectral information plays a crucial role in biological imaging, yet conventional epifluorescence and histological techniques often rely on RGB image acquisition, limiting the resolution of spectrally overlapping components. Here, we present a phasor-based spectral analysis framework adapted for RGB images, enabling unsupervised segmentation and unmixing without the need for hyperspectral systems or sequential acquisition. By applying a discrete Fourier transform to the red, green, and blue intensities at each pixel, we generate a two-dimensional phasor plot where spectral relationships are encoded in modulation and phase. We demonstrate the utility of this approach across three distinct applications: segmentation of lung histology images stained with hematoxylin and eosin to quantify alveolar collapse, analysis of autofluorescence in skin lesions (nevi and melanoma) to highlight pathological spectral signatures, and spectral unmixing in multicolor-labeled U2OS cells to resolve overlapping fluorophores. Our method improves signal separation, reduces noise, and enhances biological interpretability using standard RGB acquisition. These findings establish RGB phasor analysis as a practical and powerful tool for spectral decomposition and segmentation in microscopy, bridging the gap between conventional imaging and advanced spectral analysis.
