Development of a Cascade Processing Method for Microarray Spot Segmentation

A new method is proposed for improving microarray spot segmentation for gene quantification. The method introduces a novel combination of three image processing stages, applied locally to each spot image: i/ Fuzzy C-Means unsupervised clustering, for automatic spot background noise estimation, ii/ power spectrum deconvolution filter design, employing background noise information, for spot image restoration, iii/ Gradient-Vector-Flow (GVF-Snake), for spot boundary delineation. Microarray images used in this study comprised a publicly available dataset obtained from the database of the MicroArray Genome Imaging & Clustering Tool website. The proposed method performed better than the GVF-Snake algorithm (Kullback-Liebler metric: 0.0305 bits against 0.0194 bits) and the SPOT commercial software (pairwise mean absolute error between replicates: 0.234 against 0.303). Application of efficient adaptive spot-image restoration on cDNA microarray images improves spot segmentation and subsequent gene quantification.

[1]  Carlos Caldas,et al.  Microarray segmentation methods significantly influence data precision. , 2004, Nucleic acids research.

[2]  David Botstein,et al.  Probing Lymphocyte Biology by Genomic-Scale Gene Expression Analysis , 1998, Journal of Clinical Immunology.

[3]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[4]  Jesús Angulo,et al.  Automatic analysis of DNA microarray images using mathematical morphology , 2003, Bioinform..

[5]  Artyom M. Grigoryan,et al.  /spl alpha/-rooting image enhancement by paired splitting-signals , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[6]  Mario Mastriani,et al.  Microarrays Denoising via Smoothing of Coefficients in Wavelet Domain , 2007, 1807.11571.

[7]  A. Bowman,et al.  Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations , 1999 .

[8]  Joann M. Moreno,et al.  New methods of image enhancement , 2005, SPIE Defense + Commercial Sensing.

[9]  Yoganand Balagurunathan,et al.  Noise factor analysis for cDNA microarrays. , 2004, Journal of biomedical optics.

[10]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[11]  S. Kullback,et al.  Information Theory and Statistics , 1959 .

[12]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Mark Schena,et al.  Microarray Biochip Technology , 2000 .

[14]  R.S.H. Istepanian,et al.  Microarray image enhancement by denoising using stationary wavelet transform , 2003, IEEE Transactions on NanoBioscience.

[15]  Richard D. Deveaux,et al.  Applied Smoothing Techniques for Data Analysis , 1999, Technometrics.

[16]  Spiros Kostopoulos,et al.  Improving Microarray Spots Segmentation by K-Means driven Adaptive Image Restoration , 2006 .

[17]  Rastislav Lukac,et al.  APPLICATION OF THE ADAPTIVE CENTER-WEIGHTED VECTOR MEDIAN FRAMEWORK FOR THE ENHANCEMENT OF CDNA MICROARRAY IMAGES , 2003 .

[18]  Nikolas P. Galatsanos,et al.  Mixture model analysis of DNA microarray images , 2005, IEEE Transactions on Medical Imaging.

[19]  Rastislav Lukac,et al.  cDNA microarray image processing using fuzzy vector filtering framework , 2005, Fuzzy Sets Syst..

[20]  Terence P. Speed,et al.  Comparison of Methods for Image Analysis on cDNA Microarray Data , 2002 .