Development of a Cascade Processing Method for Microarray Spot Segmentation

  • Authors:

  • Antonis Daskalakis;Dionisis Cavouras;Panagiotis Bougioukos;Spiros Kostopoulos;Ioannis Kalatzis;George C. Kagadis;George Nikiforidis

  • Affiliations:

  • Department of Medical Physics, School of Medicine, University of Patras, Rio, GR-26503, Greece;Medical Signal and Image Processing Lab, Department of Medical, Instrumentation Technology,Technological Education Institution of Athens, Ag., Spyridonos Street, Aigaleo, 122 10, Athens, Greece;Department of Medical Physics, School of Medicine, University of Patras, Rio, GR-26503, Greece;Department of Medical Physics, School of Medicine, University of Patras, Rio, GR-26503, Greece;Medical Signal and Image Processing Lab, Department of Medical, Instrumentation Technology,Technological Education Institution of Athens, Ag., Spyridonos Street, Aigaleo, 122 10, Athens, Greece;Department of Medical Physics, School of Medicine, University of Patras, Rio, GR-26503, Greece;Department of Medical Physics, School of Medicine, University of Patras, Rio, GR-26503, Greece

  • Venue:
  • IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
  • Year:
  • 2007

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Abstract

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.