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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Jun 20, 2026

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Segmentation of complementary DNA microarray images by wavelet-based Markov random field model.

Emmanouil I Athanasiadis¹, Dionisis A Cavouras, Dimitris Th Glotsos

¹Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medical Science, University of Patras, 26 500 Rio Patras, Greece. mathan@upatras.gr

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society

|September 29, 2009

Summary

This summary is machine-generated.

A new wavelet-based Markov random field (WMRF) model accurately segments complementary DNA (cDNA) microarray images. This WMRF model outperforms existing methods in accuracy and reproducibility for gene expression analysis.

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Area of Science:

Bioinformatics
Image Processing
Computational Biology

Background:

Microarray image analysis is crucial for gene expression studies.
Accurate segmentation of spots from background noise is a key challenge.

Purpose of the Study:

To introduce a novel wavelet-based modification of the Markov random field (WMRF) model.
To evaluate the performance of the WMRF model for complementary DNA (cDNA) microarray image segmentation.

Main Methods:

The WMRF model utilizes the stationary wavelet transform (SWT) to create denoised and magnitude images.
These images are combined to segment spots from the background in microarray images.
Performance was evaluated using simulated and real microarray datasets.

Main Results:

The WMRF model demonstrated superior accuracy compared to conventional Markov random field (MRF) and Fuzzy C-means (FCM) algorithms (SMF: 92.66 vs 92.15 and 89.22; r2: 0.92 vs 0.90 and 0.84).
WMRF achieved higher reproducibility than MRF, SPOT, and SCANALYZE algorithms (MAE: 497 vs 1215, 1180, and 503; CV: 0.88 vs 1.15, 0.93, and 0.90).

Conclusions:

The proposed WMRF model offers a significant improvement for cDNA microarray image segmentation.
WMRF provides a more accurate and reproducible method for analyzing gene expression data from microarrays.