Compression of cDNA and Inkjet Microarray Images

Abstract

Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission requires compression that utilizes microarray image's structure and unique analysis goals. Hence, we have developed a progressive compression scheme for microarray images which can be either lossy or lossless. Our scheme has a coded data structure that allows fast decoding and reprocessing of image subsets, and includes summary statistics and image segmentation information. Since visual fidelity is not the end goal for microarray images, we introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. We find that a lossy compression ratio of 8:1 for cDNA microarrays minimally affects downstream processing. The average lossless compression ratio is 1.83:1 for cDNA images and 2.43:1 for inkjet images, comparable to state-of-the-art loss-less schemas, yet with added flexibility and information.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA407645

Entities

People

  • Bin Yu
  • Kannan Ramchandran
  • Rebeka Jornsten
  • Wei Wang

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Coding
  • Compression Ratio
  • Computer Programming
  • Computer Vision
  • Data Science
  • Distortion
  • Dna Microarrays
  • Extraction
  • Gene Expression
  • Information Processing
  • Information Science
  • Mrna
  • Sensitivity
  • Shape
  • Standards
  • Statistics

Readers

  • Image Processing and Computer Vision.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML