Applied Bioinformatics

, Volume 4, Issue 1, pp 1–11 | Cite as

Spot Detection and Image Segmentation in DNA Microarray Data

  • Li Qin
  • Luis RuedaEmail author
  • Adnan Ali
  • Alioune Ngom


Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.


Image Segmentation Background Correction Foreground Pixel Microarray Image Noisy Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the referees who devoted their efforts to substantially improving the quality of the paper. This research work has been partially supported by NSERC (Natural Sciences and Engineering Council of Canada), CFI (Canadian Foundation for Innovation) and OIT (Ontario Innovation Trust).

The authors have provided no information on conflicts of interest directly relevant to the content of this article..


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Copyright information

© Adis Data Information BV 2005

Authors and Affiliations

  1. 1.IBM Canada LtdMarkhamCanada
  2. 2.School of Computer ScienceUniversity of WindsorWindsorCanada
  3. 3.Department of Biological SciencesUniversity of WindsorWindsorCanada

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