Automated segmentation of cancer cell nuclei in complex tissue sections
Loukas CG., Wilson GD., Vojnovic B.
Characterisation of the proliferative activity of a tumour has been the subject of research for many years. The majority of the studies presented so far in the field of cytology and histology relates to the analysis of information from a limited number of cells, which are often easily distinguishable from the background and as well as from each other. The present paper introduces an automated image analysis technique for classification of cancer cell nuclei stained with proliferative markers. The images under processing were characterised by a high degree of complexity, containing considerable histological noise. The first step of the method aims to identify nuclear features of proliferating cells only, contained in large-scale histological images, using Principal Components Analysis (PCA). The histogram of the component that demonstrates the best contrast is processed appropriately for generating a binary image. Some standard morphological operations are then applied to remove any irrelevant structures and detect touching and/or overlapping nuclei. Two separate methods, Skeleton by Influence Zone and heuristic processing, are presented for segmentation of clustered cells. The algorithm was tested on tissue section images encountered in routine clinical practice with very encouraging results, after comparing image analysis and human observer cell counting.