Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy.

ABSTRACT:

Motivation

Current spectral unmixing methods for multiplex fluorescence microscopy have a limited ability to cope with high spectral overlap as they only analyze spectral information over individual pixels. Here, we present adaptive Morphologically Constrained Spectral Unmixing (MCSU) algorithms that overcome this limitation by exploiting morphological differences between sub-cellular structures, and their local spatial context.

Results

The proposed method was effective at improving spectral unmixing performance by exploiting: (i) a set of dictionary-based models for object morphologies learned from the image data; and (ii) models of spatial context learned from the image data using a total variation algorithm. The method was evaluated on multi-spectral images of multiplex-l

SUBMITTER: Megjhani M 

PROVIDER: S-EPMC5951100 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature