Computerized Medical Imaging and Graphics
Volume 33, Issue 1 , Pages 40-49, January 2009

An automated method for gridding and clustering-based segmentation of cDNA microarray images

  • Nikolaos Giannakeas

      Affiliations

    • Laboratory of Biological Chemistry, Medical School, University of Ioannina, Ioannina, Greece
    • Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Ioannina, Greece
    • ,
  • Dimitrios I. Fotiadis

      Affiliations

    • Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Ioannina, Greece
    • Biomedical Research Institute - FORTH, Ioannina, Greece
    • Corresponding Author InformationCorresponding author at: Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, PO Box 1186, GR 451 10 Ioannina, Greece. Tel.: +30 26510 98803; fax: +30 26510 98889.

Received 24 March 2008; received in revised form 18 September 2008; accepted 6 October 2008.

Abstract 

Microarrays are widely used to quantify gene expression levels. Microarray image analysis is one of the tools, which are necessary when dealing with vast amounts of biological data. In this work we propose a new method for the automated analysis of microarray images. The proposed method consists of two stages: gridding and segmentation. Initially, the microarray images are preprocessed using template matching, and block and spot finding takes place. Then, the non-expressed spots are detected and a grid is fit on the image using a Voronoi diagram. In the segmentation stage, K-means and Fuzzy C means (FCM) clustering are employed. The proposed method was evaluated using images from the Stanford Microarray Database (SMD). The results that are presented in the segmentation stage show the efficiency of our Fuzzy C means-based work compared to the two already developed K-means-based methods. The proposed method can handle images with artefacts and it is fully automated.

Keywords: Microarray image processing, Gridding, Segmentation, K-means, Fuzzy C means

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PII: S0895-6111(08)00101-8

doi:10.1016/j.compmedimag.2008.10.003

Computerized Medical Imaging and Graphics
Volume 33, Issue 1 , Pages 40-49, January 2009

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