Open AccessAlgorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes
Author(s) -
Rhea Chitalia,
Joshua Mueller,
Hai Fu,
Melodi Javid Whitley,
David G. Kirsch,
J. Quincy Brown,
Rebecca Willett,
Nimmi Ramanujam
Publication year - 2016
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.7.003412
Subject(s) - thresholding , microscope , fluorescence microscope , segmentation , microscopy , artificial intelligence , computer science , image segmentation , computer vision , fluorescence , optical microscope , contrast (vision) , image processing , pattern recognition (psychology) , optics , image (mathematics) , physics , scanning electron microscope
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.