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Analytical Cellular Pathology
Volume 16, Issue 1, Pages 29-43

Automatic Quantification of Immunohistochemically Stained Cell Nuclei Using Unsupervised Image Analysis

Petter Ranefall,1 Kenneth Wester,2 and Ewert Bengtsson1

1Centre for Image Analysis, Uppsala University, Sweden
2Department of Pathology, Uppsala University, Sweden

Received 16 January 1997; Accepted 2 January 1998

Copyright © 1998 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions is presented. The image is transformed by a principal component transform. The resulting first component image is used to segment the objects from the background using dynamic thresholding of the P2/A‐histogram, where P2/A is a global roundness measure. Then the image is transformed into principal component hue, defined as the angle around the first principal component. This image is used to segment positive and negative objects. The method is fully automatic and the principal component approach makes it robust with respect to illumination and focus settings. An independent test set consisting of images grabbed with different focus and illumination for each field of view was used to test the method, and the proposed method showed less variation than the intraoperator variation using supervised Maximum Likelihood classification.