Network cycle features in relative neighborhood graphs

1. A method for analyzing biomedical data comprising:

obtaining macroscopic imaging data;

obtaining histopathological imaging data;

generating a relative neighborhood graph from the histopathological imaging data by parallel computation;

executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of an Urquhart-approximation of the relative neighborhood graph;

quadtree-based partitioning of nuclei coordinates obtained from the histopathological imaging data across a plurality of processes;

Morton ordering of the nuclei coordinates to optimize load balance;

creating a shared boundary layer, wherein each of the processes Delaunay triangulates the shared boundary layer, and wherein remaining local points are added to each process's respective triangulations;

pruning the triangulations to generate the relative neighborhood graph;

registering the macroscopic imaging data and the histopathological imaging data; and

correlating the macroscopic imaging data and the network cycle features.

2. The method of claim 1 wherein the network cycle features are selected from the group consisting of cycle order, cycle perimeter, and a combination thereof.

3. The method of claim 1 further comprising:

partitioning duplicated triangles in the shared boundary layer;

marking a longest edge of each the triangles;

merging polygons sharing marked edges locally; and

merging boundary polygons.

4. The method of claim 1 wherein the biomedical data are indicative of whether a disease is present and/or a severity of the disease.

5. The method of claim 4 wherein the disease comprises cancer.

6. The method of claim 4 wherein the disease comprises prostate cancer.

7. The method of claim 1 wherein the macroscopic imaging data are selected from the group consisting of magnetic resonance data, computed tomography data, positron emission tomography data, single photon emission tomography data, and combinations thereof.

8. The method of claim 1 wherein the macroscopic imaging data comprise dynamic contrast enhanced magnetic resonance data.

9. A system for analyzing biomedical data comprising:

a memory operable to store macroscopic imaging data and histopathological imaging data;

a processor operable to (a) generate an Urquhart-approximation of a relative neighborhood graph from the histopathological imaging data by parallel computation; (b) compute one or a plurality of network cycle features of the relative neighborhood graph by parallel computation; (c) quadtree-based partition nuclei coordinates obtained from the histopathological imaging data across a plurality of processes; (d) Morton order the nuclei coordinates to optimize load balance; (e) create a shared boundary layer, wherein each of the processes Delaunay triangulates the shared boundary layer, and wherein remaining local points are added to each process's respective triangulations; (f) prune the triangulations to generate the relative neighborhood graph; and (g) correlate the macroscopic imaging data and the network cycle features; and

a display operable to display an analysis result derived from correlation of the macroscopic imaging data and the network cycle features.

10. The system of claim 9 wherein the network cycle features are selected from the group consisting of cycle order, cycle perimeter, and a combination thereof.

11. The system of claim 9 further comprising a user input operable to receive input from a system user.

12. The system of claim 9 wherein the biomedical data are indicative of whether a disease is present and/or a severity of the disease.

13. The system of claim 12 wherein the disease comprises cancer.

14. The system of claim 12 wherein the disease comprises prostate cancer.

15. The system of claim 9 wherein the macroscopic imaging data are selected from the group consisting of magnetic resonance data, computed tomography data, positron emission tomography data, single photon emission tomography data, and combinations thereof.

16. The system of claim 9 wherein the macroscopic imaging data comprise dynamic contrast enhanced magnetic resonance data.

17. A non-transitory computer readable storage medium having stored therein data representing instructions executable by a programmed processor for biomedical study, the storage medium comprising instructions for:

generating an Urquhart-approximation of a relative neighborhood graph from histopathological imaging data by parallel computation;

computing one or a plurality of network cycle features of the relative neighborhood graph by parallel computation;

quadtree-based partitioning of nuclei coordinates obtained from the histopathological imaging data across a plurality of processes;

Morton ordering of the nuclei coordinates to optimize load balance;

creating a shared boundary layer, wherein each of the processes Delaunay triangulates the shared boundary layer, and wherein remaining local points are added to each process's respective triangulations;

pruning the triangulations to generate the relative neighborhood graph; and

correlating the macroscopic imaging data and the network cycle features.

18. The non-transitory computer readable storage medium or claim 17 wherein the storage medium further comprises instructions for:

preparing an Urquhart graph from the Delaunay triangulations;

partitioning duplicated triangles in the shared boundary layer;

marking a longest edge of each triangle;

merging polygons sharing marked edges locally; and

merging boundary polygons.

19. A method for analyzing biomedical data comprising:

(a) obtaining histopathological imaging data;

(b) generating a relative neighborhood graph from the histopathological imaging data by parallel computation;

(c) executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of an Urquhart-approximation of the relative neighborhood graph;

(d) quadtree-based partitioning of nuclei coordinates obtained from the histopathological imaging data across a plurality of processes;

(e) Morton ordering of the nuclei coordinates to optimize load balance;

(f) creating a shared boundary layer, wherein each of the processes Delaunay triangulates the shared boundary layer, and wherein remaining local points are added to each process's respective triangulations;

(g) pruning the triangulations to generate the relative neighborhood graph; and

(h) predicting a presence and/or severity of a disease using the network cycle features, and/or correlating graph-theoretic microscopic biomarkers with the presence and/or severity of the disease.

20. The method of claim 19 wherein the disease comprises cancer.