Abstract: Digital image analysis can simultaneously measure many multidimensional features of each data point within an image. Each data point can then be grouped into categories, or ‘clusters’, of data points by assessing all features of each data point and measuring similarity among all data points. Clustering multidimensional data allows one to visualize the structure of their data and visually represent groups of data points in a lower dimensional space, such as a 2D or 3D graph. If the data points are not tagged with a description before clustering, it is difficult to assess which data points belong to which cluster. In this method, we describe the clustering of tissue objects (data points) into clusters based on their image analysis features, then creating a cluster map in order to describe tissue objects based on cluster association.