Automatic quantification of immunohistochemically stained cell nuclei using unsupervised image analysis.
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.
Ranefall
P
P
Centre for Image Analysis, Uppsala University, Sweden. Petter.Ranefall@cb.uu.se
Wester
K
K
Bengtsson
E
E
D000465:Algorithms; D002467:Cell Nucleus / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D007150:Immunohistochemistry / Q000379:methods* / Q000706:statistics & numerical data; D008432:Mathematical Computing; D015588:Observer Variation; D014066:Palatine Tonsil / Q000473:pathology; D013194:Staining and Labeling / Q000379:methods* / Q000706:statistics & numerical data; D001749:Urinary Bladder Neoplasms / Q000473:pathology