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Medical and Biological Engineering and Computing

, Volume 43, Issue 5, pp 672–677 | Cite as

Automatic analysis of immunocytochemically stained tissue samples

  • F. Arámbula Cosío
  • J. A. Márquez Flores
  • M. A. Padilla Castañeda
  • S. Solano
  • P. Tato
Article

Abstract

An automatic colour image segmentation and cell counting software system has been developed for immunocytochemical analysis of stained tissue samples. The system was designed to count the total number of positive and negative cells in tissue samples treated with cytokine DNA probes from pigs naturally parasitised with Taenia solium metacestodes, using in situ hybridisation. A reaction index was calculated as the ratio of the number of cells with a positive reaction to the total number of cells (positives plus negatives) for each of five different probes. The objectives of automatic counting were to improve the reproducibility of the analysis and reduce the processing time of large image batches. A fast KNN classifier was used for colour segmentation. Watershed segmentation combined with edge detection was used to isolate individual cells that were then automatically labelled, using the results of the corresponding colour segmented image. Validation was performed on 122 non-training digital images with a total of 1069 positive cells and 1459 negative cells, with the following results: a mean true positive rate of 90.2% for positive cells and a mean true positive rate of 85.4% for negative cells. The corresponding mean false positive rates were 9.6% and 6.6%. The mean reaction index error of the automatic analysis was 5.35%. The processing of each digital image took 10 s on a Pentium IV PC.

Keywords

Automatic immunocytochemical analysis Automatic cell counting Colour classification Watershed segmentation 

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Copyright information

© IFMBE 2005

Authors and Affiliations

  • F. Arámbula Cosío
    • 1
  • J. A. Márquez Flores
    • 1
  • M. A. Padilla Castañeda
    • 1
  • S. Solano
    • 2
  • P. Tato
    • 2
  1. 1.Image Analysis & Visualisation LabCCADET, UNAMMéxico
  2. 2.Department of Microbiology & Parasitology, Faculty of MedicineUNAMMéxico

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