1. A method to acquire and process vitreous image data to characterize vitreous inhomogeneities comprising the steps of:
creating a unit, said creating a unit comprising transmitting at least one ultrasound signal, acquiring phase-resolved, raw backscatter echo data of said transmitted signal, and normalizing said acquired data, wherein said normalized acquired data forms said unit; the method further comprising:
extracting local level estimates from said normalized data; and
processing said extracted local level estimates, said processing producing at least one global parameter, said at least one global parameter being used to objectively characterize one or more vitreous inhomogeneities.
2. The method of claim 1, wherein said global parameter is produced from one local-level estimate from within a vitreous of an eye.
3. The method of claim 1, wherein said global parameter is produced from a plurality of local level estimates from within a vitreous of an eye.
4. The method of claim 1, further comprising repeating the step of creating a unit until 2D single cross-sectional image-plane data of an entire vitreous is obtained.
5. The method of claim 4, further comprising preparing an image volume by compiling a plurality of said 2D single cross-sectional image plane data.
6. The method of claim 1, wherein said ultrasound signal is transmitted from an annular array.
7. The method of claim 6, further comprising applying a synthetic focusing approach to said acquired data.
8. The method of claim 1, further comprising calculating an envelope of said phase-resolved, raw backscatter echo data.
9. The method of claim 1, wherein said ultrasound signal is transmitted from a single-element.
10. The method of claim 1, further comprising a step of segmenting the vitreous out from surrounding tissue prior to extracting local-level estimates.
11. A method to quantify vitreous image data to characterize inhomogeneities of a vitreous comprising the steps of:
transmitting at least one ultrasound signal;
acquiring an envelope of phase-resolved, raw backscatter echo data of said transmitted signal;
normalizing said acquired data;
extracting local level estimates from said normalized data; and
combining said local level estimates producing at least one global parameter used to characterize vitreous inhomogeneities, said at least one global parameter being incorporated into a classifier to yield at least one optimal global index or end value indicative of an overall vitreous pathology.
12. The method of claim 11, further comprising creating a normal database, wherein said normal database is a compilation of first extracted global parameters being vitreous parameter data representing a normal vitreous.
13. The method of claim 12, further comprising creating a disease database, wherein said disease database is a compilation of second extracted global parameters being vitreous parameter data representing diseased vitreous conditions at various stages of disease.
14. The method of claim 13, wherein said at least one global parameter is compared with said normal database and said disease database, said comparison identifying a correlation between said global parameter and information in one of two said databases, said correlation being used to diagnose one or more vitreous conditions ranging from age-normal to various stages of diseased states, said diagnosis being further comparable with said optimal global index or end value.
15. A method to quantify vitreous image data to characterize vitreous inhomogeneities comprising the steps of:
creating a unit, said creating a unit comprising transmitting at least one ultrasound signal, acquiring phase-resolved, raw backscatter echo data of said transmitted signal, and normalizing said acquired data;
extracting local-level estimates from said normalized data;
combining said extracted local-level estimates producing at least one global parameter, said at least one global parameter being incorporated into a classifier to yield an end value indicative of an overall vitreous pathology;
creating a normal database, wherein said normal database is a compilation of first extracted global parameters being vitreous parameter data representing normal vitreous;
creating a disease database, wherein said disease database is a compilation of second extracted global parameters being vitreous parameter data representing diseased vitreous conditions at various stages of disease; and
diagnosing a vitreous condition based on comparison of said normal database with said disease database.
16. The method of claim 15, further comprising repeating the step of creating a unit until 2D single cross-sectional image plane data of an entire vitreous is obtained.
17. The method of claim 16, further comprising preparing an image volume by compiling a plurality of said 2D single cross-sectional image plane data.
18. The method of claim 15, wherein said at least one transmitted ultrasound signal is transmitted from an annular array.
19. The method of claim 18, further comprising applying a synthetic focusing approach to said acquired backscatter signal.
20. The method of claim 18, further comprising calculating an envelope of said phase-resolved, raw backscatter echo data.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A decision support system assessing an intervention action in response to a hazard situation developing in a physical domain, the decision support system comprising:
a hazard simulator configured to generate and output information representing the hazard situation by combining data from a plurality of data sources describing one of the hazard situation and the physical domain including at least one source of real-time data from the physical domain;
an agent based modeler configured to run a simulation using rules for predicting the behavior of agents affected by the hazard situation to predict the behavior of the individual agents in response to the hazard situation;
a decision selector configured to select one intervention action from a set of candidate intervention actions in dependence upon the behavior predicted by the agent based modeler;
an iterative decision tester configured to test the selected intervention action by, for each of a predetermined set of possible outcomes of the selected intervention action, initiating the agent-based modeler to reiterate the simulation with the respective possible outcome as a factor influencing agent behavior; and
an assessor configured to assess the behavior of the agents predicted by each iteration of the simulation to determine whether to initiate the selected intervention action.
2. A decision support system according to claim 1, comprising:
a rule generator configured to use the information output by the hazard simulator to generate rules for predicting the behavior of agents affected by the hazard situation.
3. A decision support system according to claim 1, wherein the hazard simulator comprises:
a federated database server configured to retrieve data from the plurality of data sources, and
a data exploration engine configured to analyze the retrieved data in order to generate the information representing the hazard situation.
4. A decision support system according to claim 3, wherein the plurality of data sources includes one or more of the following:
a data source storing date representing the socio-economic properties of occupants of the physical domain;
a data source storing data representing environmental conditions in the physical domain;
a data source storing candidate intervention actions;
a data source storing a representation of geographical information in the physical domain including one or more of land surface information, vegetation surface information, river surface information, ocean information including information of at least one of a tide, a wave height, and air information including pollution and visibility;
a data source storing a representation of man-made structures in the physical domain;
a data source storing implementation options for each candidate intervention action; and
a data source storing data representing hazard prediction information.
5. A decision support system according to claim 1, wherein the decision selector is configured to select more than one from the set of candidate intervention actions in dependence upon the behavior predicted by the agent based modeler;
the iterative decision tester is configured to test each of the more than one selected intervention actions; and
the assessor is configured to determine which one of the selected intervention actions to initiate based on the assessment of the behavior of the agents predicted by each iteration of the simulation.
6. A decision support system according to claim 5, wherein the iterative decision tester is configured to test the more than one of the selected intervention actions in combination with another one of the selected intervention actions in iterations of the simulation.
7. A decision support system according to claim 1, wherein the iterative decision tester is configured to access a plurality of implementation options associated with the selected intervention action, and for each implementation option, to reiterate the simulation with each possible outcome, the respective implementation option and the respective possible outcome each being factors influencing agent behavior; and
the assessor is configured to select an implementation option for the selected intervention action based on the assessment of the behavior of the agents predicted by each iteration of the simulation.
8. A decision support system according to claim 1, further comprising:
a communicator configured to output a representation of the intervention action determined to be initiated, by the assessor, and the selected implementation option to one of a user interface, one or more recipients.
9. A decision support system according to claim 1, wherein the plurality of data sources includes an intervention action, and a previously determined implementation option to be initiated by the assessor, and the information output by the hazard simulator includes an indication of the previously determined intervention action, and a corresponding implementation option.
10. A decision support system according to claim 1, wherein the assessor is configured to assess the behavior of agents predicted by each iteration of the simulation by comparing the predicted behavior with one or more behavior goals and quantifying whether each of the behavior goals is satisfied by the predicted behavior.
11. A decision support system according to claim 1, wherein the assessor is configured to obtain a representation of cost associated with the selected intervention action, and each implementation option, where cost information is included as a factor in determining whether to initiate the selected intervention action andor which implementation option to select.
12. A decision support system according to claim 1, comprising:
a rule generator is configured to combine rule definitions from an external agent library with information output by the hazard simulator to generate an agent library specific to the hazard situation and the physical domain including the rules for predicting the behavior of agents affected by the hazard situation.
13. A decision support method for assessing an intervention action in response to a hazard situation developing in a physical domain, the decision support method comprising:
generating and outputting information representing the hazard situation by combining data from a plurality of data sources describing the hazard situation andor the physical domain including at least one source of real-time data from the physical domain;
generating rules, using the output information, for predicting the behavior of agents affected by the hazard situation;
executing a simulation using an agent based modeling simulator and generated rules to predict the behavior of the agents in response to the hazard situation;
selecting one intervention action from a set of candidate intervention actions in dependence upon the predicted behavior;
testing the selected intervention action, for each of a predetermined set of possible outcomes of the selected intervention action, by initiating further simulations by the agent based modeling simulator with a respective possible outcome as a factor influencing agent behavior; and
assessing the behavior of the agents predicted by each iteration of the simulation and determining whether to initiate the selected intervention action.
14. A non-transitory computer readable storage medium storing a computer program which, when executed by a computing apparatus, causes the computing apparatus to function as the decision support system of claim 1.
15. A non-transitory computer readable storage medium storing a computer program which, when executed by a computing apparatus, causes the computing apparatus to perform the method of claim 13.