Objective: To explore tissue organization based on the geometry of cell neighborhoods in histologic preparations.
Study design: Local complexity of solid tissues was measured in images of discrete tissue compartments. Exclusive areas associated with cell nuclei (v-cells) were computed using a watershed transform of the nuclear staining intensity. Mathematical morphology was used to define neighborhood membership, distances and identify complete nested neighborhoods. Neighborhood complexity was estimated as the scaling of the number of neighbors relative to reference v-cells.
Results: The methodology applied to hematoxylin-eosin-stained sections from normal, dysplastic and neoplastic oral epithelium revealed that the scaling exponent, over a finite range of neighborhood levels, is nonunique and fractional. While scaling values overlapped across classes, the average was marginally higher in neoplastic than in dysplastic and normal epithelia. The best classificatory power of the exponent was 58% correct classification into 3 diagnostic classes (11 levels) and 83% between dysplastic and neoplastic classes (13 levels).
Conclusion: V-cell architecture retains features of the original tissue classes and demonstrates an increase in tissue disorder in neoplasia. This methodology seems suitable for extracting information from tissues where identification of cell boundaries (and therefore segmentation into individual cells) is unfeasible.