Abstract: A method and apparatus for tissue modeling using at least one tissue image having cells therein and derived from biological tissue. Data derived from the tissue image is clustered to generate cluster vectors such that each cluster vector represents a portion of the tissue image. Cell information is generated which assigns a cell class or a background class to each of the cluster vectors. A cell-graph is generated for the tissue image from the generated cell information. The generated cell-graph comprises nodes and edges. The edges connect at least two of the nodes together. Each node represents at least one cell of the biological tissue or a portion of a single cell of the biological tissue. At least one metric may be computed from the nodes and edges, and the biological tissue may be classified based on the at least one metric.

Abstract: A method and apparatus for tissue modeling using at least one tissue image derived from clinical tissue. The at least one tissue image having cells therein. The method comprises for each tissue image of the at least one tissue image wherein each tissue image is denoted as a sample tissue image: clustering data derived from the sample tissue image to generate cluster vectors, each cluster vector representing of portion of the tissue image; generating cell information, comprising assigning a cell class or a background class to each of the cluster vectors; generating a cell-graph for the sample tissue image from using the generated cell information, said cell-graph comprising nodes and edges, said edges connecting some of the cell nodes together based on a connectivity criterion; and computing at least one metric from the generated cell-graph.