title: Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium.
abstract: BACKGROUND
  Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.
  
  
  METHODS
  Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.
  
  
  RESULTS
  Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.
  
  
  CONCLUSIONS
  The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.
  The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995.
authors.affiliation: Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. juho.konsti@helsinki.fi
authors.forename: Juho
authors.initials: J
authors.lastname: Konsti
authors.forename: Mikael
authors.initials: M
authors.lastname: Lundin
authors.forename: Nina
authors.initials: N
authors.lastname: Linder
authors.forename: Caj
authors.initials: C
authors.lastname: Haglund
authors.forename: Carl
authors.initials: C
authors.lastname: Blomqvist
authors.forename: Heli
authors.initials: H
authors.lastname: Nevanlinna
authors.forename: Kirsimari
authors.initials: K
authors.lastname: Aaltonen
authors.forename: Stig
authors.initials: S
authors.lastname: Nordling
authors.forename: Johan
authors.initials: J
authors.lastname: Lundin
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000378:metabolism / Q000473:pathology*; D015179:Colorectal Neoplasms / Q000378:metabolism / Q000473:pathology*; D044962:Data Compression / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D004848:Epithelium / Q000378:metabolism / Q000473:pathology; D066246:ErbB Receptors / Q000378:metabolism; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry / Q000379:methods*; D019394:Ki-67 Antigen / Q000378:metabolism; D046228:Microarray Analysis; D012189:Retrospective Studies; D013194:Staining and Labeling / Q000379:methods*
chemical_list: D019394:Ki-67 Antigen; D066246:ErbB Receptors