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Published on: February 15, 2022
An automated method for gridding and clustering-based segmentation of cDNA microarray images
Nikolaos Giannakeas1, Dimitrios I Fotiadis
1Laboratory of Biological Chemistry, Medical School, University of Ioannina, Ioannina, Greece.
Summary
This study introduces an automated method for microarray image analysis, improving gene expression quantification. The new Fuzzy C-means approach enhances spot segmentation accuracy compared to existing K-means techniques.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Microarrays are essential for quantifying gene expression levels.
- Analyzing vast biological data from microarrays requires robust image analysis tools.
Purpose of the Study:
- To propose a novel, fully automated method for microarray image analysis.
- To enhance the accuracy of gene expression quantification through improved image segmentation.
Main Methods:
- The method involves two stages: gridding and segmentation.
- Preprocessing includes template matching, block and spot finding, and Voronoi diagram-based gridding.
- Segmentation utilizes K-means and Fuzzy C-means (FCM) clustering algorithms.
Main Results:
- The Fuzzy C-means (FCM) based segmentation demonstrated superior efficiency compared to K-means methods.
- The automated method effectively handles microarray images containing artefacts.
- Evaluation using Stanford Microarray Database (SMD) images confirmed the method's effectiveness.
Conclusions:
- The proposed automated microarray image analysis method, particularly using Fuzzy C-means (FCM), offers improved segmentation accuracy.
- This automated approach is valuable for high-throughput gene expression studies.
- The method's robustness in handling artefacts makes it a reliable tool for biological data analysis.
