A study on microarray image gridding techniques for DNA analysis

Microarray is one of the most promising tools available for researchers in the life sciences to study gene expression profiles. Through microarray analysis, gene expression levels can be obtained, and the biological information of a disease can be identified. The gene expression information embedded in the microarray is extracted using image-processing techniques. Gridding is one of the important processes used to extract features in DNA microarray, by assigning each spot in the microarray with individual coordinates for further data interpretation. This paper evaluates popular techniques of DNA microarray image gridding in the literature with an emphasis on gridding accuracy, speed, and the ability to remove noise. Based on our evaluation, the Otsu method can provide a better performance in terms of processing speed, accuracy, and ability to remove noise compared to other methods discussed in this paper.

[1]  Franz Kummert,et al.  A Markov Random Field model of microarray gridding , 2003, SAC '03.

[2]  P. Goodfellow,et al.  DNA microarrays in drug discovery and development , 1999, Nature Genetics.

[3]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[4]  D. Maroulis,et al.  A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images , 2011 .

[5]  Volkan Uslan,et al.  Clustering-based spot segmentation of cDNA microarray images , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[6]  Fernando Martin-Sanchez,et al.  Microarrays and Colon Cancer in the Road for Translational Medicine , 2011 .

[7]  Don Simone Daly,et al.  Automated Microarray Image Analysis Toolbox for MATLAB , 2005, Bioinform..

[8]  David Botstein,et al.  The Stanford Microarray Database , 2001, Nucleic Acids Res..

[9]  Peter Bajcsy Gridline: automatic grid alignment DNA microarray scans , 2004, IEEE Transactions on Image Processing.

[10]  Toby P. Breckon,et al.  Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab , 2011 .

[11]  Giuliano Antoniol,et al.  A Deformable Grid-Matching Approach for Microarray Images , 2006, IEEE Transactions on Image Processing.

[12]  I. A. Fouad,et al.  Developing a new methodology for de-noising and gridding cDNA microarray images , 2012, 2012 Cairo International Biomedical Engineering Conference (CIBEC).

[13]  Eleni Zacharia,et al.  An Original Genetic Approach to the Fully Automatic Gridding of Microarray Images , 2008, IEEE Transactions on Medical Imaging.

[14]  Ajay N. Jain,et al.  Fully automatic quantification of microarray image data. , 2002, Genome research.

[15]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.