Segmentation of cDNA Microarray Spots Using Markov Random Field Modeling

Authors:
Omer Demirkaya;Musa H. Asyali;Mohamed M. Shoukri
Affiliations:
Department of Biostatistics, Epidemiology, and Scientific Computing King Faisal Specialist Hospital and Research Center MBC No. 03, PO Box 3354, Riyadh 11211, Saudi Arabia;Department of Biostatistics, Epidemiology, and Scientific Computing King Faisal Specialist Hospital and Research Center MBC No. 03, PO Box 3354, Riyadh 11211, Saudi Arabia;Department of Biostatistics, Epidemiology, and Scientific Computing King Faisal Specialist Hospital and Research Center MBC No. 03, PO Box 3354, Riyadh 11211, Saudi Arabia
Venue:
Bioinformatics
Year:
2005

Citing 0
Cited 12

Automating the processing of cDNA microarray images

International Journal of Intelligent Systems Technologies and Applications
Segmentation of cDNA microarray images by kernel density estimation

Journal of Biomedical Informatics
A robust ratio estimator of gene expression via inverse-variance weighting for cDNA microarray images

Computational Statistics & Data Analysis
A comparative study of individual and ensemble majority vote cDNA microarray image segmentation schemes, originating from a spot-adjustable based restoration framework

Computer Methods and Programs in Biomedicine
Quantitative Improvements in cDNA Microarray Spot Segmentation

BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Complementary DNA microarray image processing based on the fuzzy Gaussian mixture model

IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Segmentation of complementary DNA microarray images by wavelet-based Markov random field model

IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
A wavelet-based Markov random field segmentation model in segmenting microarray experiments

Computer Methods and Programs in Biomedicine
High performance phosphorylation site assignment algorithm for mass spectrometry data using multicore systems

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A novel neural network approach to cDNA microarray image segmentation

Computer Methods and Programs in Biomedicine
Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Object segmentation and classification using 3-D range camera

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	3.84

Visualization

Abstract

Motivation: Spot segmentation is a critical step in microarray gene expression data analysis. Therefore, the performance of segmentation may substantially affect the results of subsequent stages of the analysis, such as the detection of differentially expressed genes. Several methods have been developed to segment microarray spots from the surrounding background. In this study, we have proposed a new approach based on Markov random field (MRF) modeling and tested its performance on simulated and real microarray images against a widely used segmentation method based on Mann--Whitney test adopted by QuantArray software (Boston, MA). Spot addressing was performed using QuantArray. We have also devised a simulation method to generate microarray images with realistic features. Such images can be used as gold standards for the purposes of testing and comparing different segmentation methods, and optimizing segmentation parameters. Results: Experiments on simulated and 14 actual microarray image sets show that the proposed MRF-based segmentation method can detect spot areas and estimate spot intensities with higher accuracy. Availability: The algorithms were implemented in Matlab™ (The Mathworks, Inc., Natick, MA) environment. The codes for MRF-based segmentation and image simulation methods are available upon request. Contact: demirkaya@ieee.org