Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes.

Published online

Journal Article

Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems.

Full Text

Duke Authors

Cited Authors

  • Chitalia, R; Mueller, J; Fu, HL; Whitley, MJ; Kirsch, DG; Brown, JQ; Willett, R; Ramanujam, N

Published Date

  • September 1, 2016

Published In

Volume / Issue

  • 7 / 9

Start / End Page

  • 3412 - 3424

PubMed ID

  • 27699108

Pubmed Central ID

  • 27699108

International Standard Serial Number (ISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/BOE.7.003412


  • eng

Conference Location

  • United States