Analysis-driven lossy compression of DNA microarray images

Miguel Hernández-Cabronero, Ian Blanes, Armando J. Pinho, Michael W. Marcellin, Joan Serra-Sagristà

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


DNA microarrays are one of the fastest-growing new technologies in the field of genetic research, and DNA microarray images continue to grow in number and size. Since analysis techniques are under active and ongoing development, storage, transmission and sharing of DNA microarray images need be addressed, with compression playing a significant role. However, existing loss-less coding algorithms yield only limited compression performance (compression ratios below 2:1), whereas lossy coding methods may introduce unacceptable distortions in the analysis process. This work introduces a novel Relative Quantizer (RQ), which employs non-uniform quantization intervals designed for improved compression while bounding the impact on the DNA microarray analysis. This quantizer constrains the maximum relative error introduced into quantized imagery, devoting higher precision to pixels critical to the analysis process. For suitable parameter choices, the resulting variations in the DNA microarray analysis are less than half of those inherent to the experimental variability. Experimental results reveal that appropriate analysis can still be performed for average compression ratios exceeding 4.5:1.

Original languageEnglish (US)
Article number2489262
Pages (from-to)654-664
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number2
StatePublished - Feb 2016


  • DNA microarray images
  • Image compression
  • Quantization

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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