Fluorescence microscopy image segmentation based on graph and fuzzy methods: A comparison with ensemble method

Accurate segmentation of fluorescence images has become increasingly important for recognizing cell nucleus that have the phenotype of interest in biomedical applications. In this study an ensemble based method is proposed for the segmentation of cell cancer microscopy images. The ensemble is constr...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2018-01, Vol.34 (4), p.2563-2578
Main Authors: Beheshti, Maedeh, Ashapure, Akash, Rahnemoonfar, Maryam, Faichney, Jolon
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Biomedical materials
Confidence intervals
Fluorescence
Fuzzy sets
Image segmentation
Microscopy
Nuclei (cytology)
Object recognition
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate segmentation of fluorescence images has become increasingly important for recognizing cell nucleus that have the phenotype of interest in biomedical applications. In this study an ensemble based method is proposed for the segmentation of cell cancer microscopy images. The ensemble is constructed and compared using Bayes graph-cut algorithm, binary graph-cut algorithm, spatial fuzzy C-means, and fuzzy level set algorithm, which were chosen for their accuracy and efficiency in the segmentation area. We investigate the performance of each method separately and finally compare the results with the ensemble method. Experiments are conducted over two datasets with different cell types. At 95% confidence level, the ensemble based method represents the best among all the implemented algorithms. Also ensemble method depicts better results in comparison with other state-of-the-art segmentation methods.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-17466