Extending Ripley's K-Function to Quantify Aggregation in 2-D Grayscale Images.

ABSTRACT: In this work, we describe the extension of Ripley's K-function to allow for overlapping events at very high event densities. We show that problematic edge effects introduce significant bias to the function at very high densities and small radii, and propose a simple correction method that successfully restores the function's centralization. Using simulations of homogeneous Poisson distributions of events, as well as simulations of event clustering under different conditions, we investigate various aspects of the function, including its shape-dependence and correspondence between true cluster radius and radius at which the K-function is maximized. Furthermore, we validate the utility of the function in quantifying clustering in 2-D grayscale images using three modalities: (i) Simulations of

SUBMITTER: Amgad M 

PROVIDER: S-EPMC4670231 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature