Author: claimsbot

What is claimed is:

1. A method for automated analysis of textural differences present on a diagnostic medical image, said image comprising a plurality of pixels, with each pixel having a gray level assigned thereto, said method comprising:
defining a region of interest (ROI) on said image;
performing at least one first order texture measure within said ROI to describe a frequency of occurrence of all gray levels assigned to pixels of said ROI;
performing at least one second order texture measure within said ROI to describe spatial interdependencies between the pixels of said ROI; and
classifying said ROI as belonging to a tissue pathology class based upon said first and second order texture measures obtained.
2. The method of claim 1, wherein prior to defining an ROI, said method further comprises:
removing structures within said image by assigning pixels that form said structures a particular gray level.
3. The method of claim 1, wherein prior to defining an ROI, said method further comprises:
forming pixel regions within said image, wherein said pixels that are located adjacent one another are assigned a common gray level providing that said adjacent pixels’ gray levels differ by an insignificant amount; and
performing said second order texture measure within only said ROI.
4. The method of claim 3, wherein said commonly assigned gray level is the average of all said adjacent pixels’ gray levels that differ in value by an insignificant amount.
5. The method of claim 3, wherein said adjacent pixels’ gray levels differing by an insignificant amount is a difference in gray level of 20 or less.
6. The method of claim 3, wherein prior to forming pixel regions within said image, said method further comprises:
converting said image from an 11-bit format to an 8-bit format.
7. The method of claim 1, wherein prior to said classifying, said method further comprises:
centering a pixel block about each pixel of said ROI;
determining the average of absolute gray level intensity differences of each possible pixel-pair separated by a distance d within said pixel block; and
assigning the pixel, about which said pixel block is centered, a stochastic fractal value based upon said average absolute gray level intensity differences obtained.
8. The method of claim 7, wherein said method further comprises:
assigning said pixels of said ROI only one of two binary values dependent upon their respective gray levels;
mapping said image onto a grid of super-pixels of increasing size e;
determining the number of said super-pixels that are one binary value within said ROI; and
determining a geometric fractal value of said ROI based upon said determined number of super-pixels that are said one binary value.
9. The method of claim 1, wherein prior to said classifying, said method further comprises:
eliminating said first order and second order texture measures which are redundant or fail to properly distinguish a particular tissue pathology class.
10. The method of claim 1, wherein prior to said classifying, said method further comprises:
providing known samples of particular tissue pathology classes;
performing first order and second order texture measures from said samples; and
storing said first order and second order texture measures obtained from said samples in association with said particular tissue pathology classes.
11. The method of claim 10, wherein said method further comprises:
determining the probability that said ROI belongs to a particular tissue pathology class based upon said stored texture measures and said first and second order texture measures performed on said ROI; and
classifying said ROI to the particular tissue pathology class for which said probability is the highest.
12. The method of claim 1, wherein said performing one or more second order texture measures further comprises:
determining gray level run-lengths that exist within said ROI by inspecting the existence of consecutive, collinear pixels that possess the same gray level in said ROI.
13. The method of claim 1, wherein said performing one or more second order texture measures further comprises:
determining the number of times a particular gray level and another particular gray level occur at a separation distance d within said ROI.
14. The method of claim 1, wherein said method further comprises:
assigning a color code to said ROI that is indicative of said particular tissue pathology class assigned to said ROI.
15. The method of claim 1, wherein said diagnostic medical image is a computed tomography (CT) image.
16. A method for automated analysis of a diagnostic medical image, said image comprising a plurality of pixels, with each pixel having a particular gray level assigned thereto, said method comprising:
forming pixel regions within said image by assigning a common gray level to a group of pixels which possess substantially similar gray levels;
obtaining measures from said image which describe relationships between said pixels; and
classifying areas of said image to a particular tissue pathology class based upon said texture measures obtained.
17. An apparatus, comprising:
image input means adapted to receive a diagnostic medical image, said image comprising a plurality of pixels, with each pixel having a particular gray level assigned thereto; and
processor means adapted to perform texture measures on a group of pixels within said image, said texture measures providing information on an occurance frequency of gray levels assigned to said group of pixels and spatial interdependencies between particular pixels of said group of pixels, said processor means being further adapted to classify said group of pixels to a tissue pathology class based upon said texture measures obtained.
18. The apparatus of claim 17 further comprising:
display means for displaying a graphical user interface and said received image.
19. The apparatus of claim 18 further comprising:
user input means for use in conjunction with said graphical user interface, wherein a user of said apparatus is able to select a plurality of options of said apparatus by use of said user input means and said graphical user interface.
20. The apparatus of claim 19, wherein said user input means is a computer mouse.
21. The apparatus of claim 17, wherein said processor is further adapted to remove structures within said image by assigning pixels that form said structures a particular gray level.
22. The apparatus of claim 17, wherein said processor is further adapted to form pixel regions within said image, wherein said pixels that are located adjacent one another are assigned a common gray level providing that said adjacent pixels’ gray levels differ by an insignificant amount.
23. The apparatus of claim 22, wherein said commonly assigned gray level is the average of all said adjacent pixels’ gray levels that differ in value by an insignificant amount.
24. The apparatus of claim 22, wherein said adjacent pixels’ gray levels differing by an insignificant amount is a difference in gray level of 20 or less.
25. The apparatus of claim 17, wherein said processor is further adapted to convert said received image from an 11-bit format to an 8-bit format.
26. The apparatus of claim 17, wherein said processor is further adapted to center a pixel block about each pixel of said group of pixels; determine the average of absolute gray level intensity differences of each possible pixel-pair separated by a distance d within said pixel block; and assign the pixel, about which said pixel plock is centered, a stochastic fractal value based upon said average gray level intensity differences obtained.
27. The apparatus of claim 26, wherein said processor is further adapted to assign said pixels of said group of pixels only one of two binary values dependent upon their respective gray levels; map said image onto a grid of super-pixels of increasing size e; determine the number of said super-pixels that are one binary value within said group of pixels; and determine a geometric fractal value of said group of pixels based upon said determined number of super-pixels that are said one binary value.
28. The apparatus of claim 17, wherein said processor is further adapted to eliminate said texture measures which are redundant or fail to properly distinguish a particular tissue pathology class.
29. The apparatus of claim 17, wherein said image input means is further adapted to receive samples of images that are known to possess a particular tissue pathology class; said processer is further adapted to determine texture measures from said received samples of images; and said apparatus further comprises:
storage means for storing said texture measures obtained from said samples in association with said particular tissue pathology class.
30. The apparatus of claim 29, wherein said processor is further adapted to determine the probability that said group of pixels belongs to a particular tissue pathology class based upon said stored texture measures in said storage means and said texture measures performed on said group of pixels; and said processor is further adapted to classify said group of pixels to the particular tissue pathology class for which said probability is the highest.
31. The apparatus of claim 17, wherein at least one of said texture measures is determined from gray level run-lengths that exist within said group of pixels by inspecting the existence of consecutive, collinear pixels that possess the same gray level.
32. The apparatus of claim 17, wherein at least one of said texture measures is determined from the number of times a particular gray level and another particular gray level, within said group of pixels, occur at a separation distance d.
33. The apparatus of claim 18, wherein said processor is further adapted to assign a color code to said group of pixels that is indicative of said tissue pathology class classified to said group of pixels and said display means displays said color code on said image at the location of said group of pixels.
34. The apparatus of claim 17, wherein said texture measures comprise at least one first order texture measure to describe a frequency of occurrence of all gray levels assigned to pixels of said ROI and at least one second order texture measure to obtain spatial interdependencies between the pixels of said ROI.
35. The apparatus of claim 17, wherein said diagnostic medical image is a computed tomography (CT) image.
36. A method for automated analysis of textural differences present on a diagnostic medical image, said image comprising a plurality of pixels, with each pixel having a gray level assigned thereto, said method comprising:
forming pixel regions within said image, wherein said pixels that are located adjacent one another are assigned a common gray level providing that said adjacent pixels’ gray levels differ by an insignificant amount;
defining a region of interest (ROI) on said image;
performing at least one first order texture measure within said ROI to describe a frequency of occurrence of all gray levels assigned to pixels of said ROI;
performing at least one second order texture measure within said ROI to describe spatial interdependencies between the pixels of said ROI; and
classifying said ROI as belonging to a tissue pathology class based upon said first and second order texture measures obtained.
37. A method for automated analysis of textural differences present on a diagnostic medical image of the pulmonary region, said image comprising a plurality of pixels, with each pixel having a gray level assigned thereto, said method comprising:
defining a region of interest (ROI) on said image;
performing at least one first order texture measure within said ROI to describe a frequency of occurrence of all gray levels assigned to pixels of said ROI;
performing at least one second order texture measure within said ROI to describe spatial interdependencies between the pixels of said ROI;
centering a pixel block about each pixel of said ROI;
determining the average of absolute gray level intensity differences of each possible pixel-pair separated by a distance d within said pixel block;
assigning the pixel, about which said pixel block is centered, a stochastic fractal value based upon said average absolute gray level intensity differences obtained; and
classifying said ROI as belonging to a tissue pathology class based upon said first and second order texture measures and said stochastic fractal value obtained.

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 method for automatic endovascular alignment of a prosthesis in a curved implantation site of a vessel, which comprises:
providing a prosthesis delivery system with a longitudinally movable distal delivery assembly and a longitudinally movable guidewire lumen having a curved distal portion;
loading the prosthesis in the distal delivery assembly;
positioning a guidewire at least into the curved implantation site of the vessel;
threading the guidewire lumen over the guidewire along with the loaded distal delivery assembly to advance the guidewire lumen and the loaded distal delivery assembly along the guidewire towards the curved implantation site; and
further advancing the guidewire lumen with the curved distal portion and the loaded distal delivery assembly along the guidewire and into the curved implantation site to at least approximately align the curved distal portion to a curve of the curved implantation site and, thereby, orient the prosthesis.
2. The method according to claim 1, wherein the curved distal portion defines a distal portion plane and the curved implantation site of the vessel is a curved portion of an aorta, and which further comprises carrying out the loading step by substantially aligning a given longitudinal line of the prosthesis in the distal portion plane.
3. The method according to claim 2, which further comprises:
positioning the guidewire into the curved portion of the aorta; and
carrying out the further advancing step by orienting the prosthesis to at least approximately align the given longitudinal line with a superior longitudinal surface line of the curved portion of the aorta.
4. The method according to claim 2, which further comprises:
positioning the guidewire into the curved portion of the aorta; and
carrying out the further advancing step by orienting the prosthesis to substantially align the given longitudinal line with a superior longitudinal surface line of the curved portion of the aorta.
5. The method according to claim 2, which further comprises:
providing the prosthesis with a curved longitudinal support member disposed at least in part on the given longitudinal line; and
carrying out the further advancing step by orienting the prosthesis to at least approximately align the part of the support member with a superior longitudinal surface line of the curved portion of the aorta.
6. The method according to claim 5, which further comprises carrying out the loading step by aligning at least a part of the curved longitudinal support member in the distal portion plane.
7. The method according to claim 2, which further comprises:
providing the prosthesis with a curved longitudinal support member disposed at least in part on the given longitudinal line; and
carrying out the further advancing step by orienting the prosthesis to substantially align the part of the support member with a superior longitudinal surface line of the curved portion of the aorta.
8. The method according to claim 7, which further comprises carrying out the loading step by aligning at least a substantial part of the curved longitudinal support member in the distal portion plane.
9. The method according to claim 1, which further comprises carrying out the further advancing step by substantially aligning the curved distal portion with the curve of the curved implantation site.
10. The method according to claim 1, which further comprises carrying out the further advancing step by at least approximately aligning the curved distal portion with the curve of the curved implantation site by rotating the distal delivery assembly and the prosthesis loaded therein.
11. The method according to claim 1, which further comprises carrying out the further advancing step by orienting the prosthesis in a optimal superior surface position.
12. The method according to claim 1, which further comprises carrying out the further advancing step by automatically aligning the prosthesis in a optimal superior surface position while traversing a curve of the vessel in which the prosthesis is to be implanted.
13. The method according to claim 5, which further comprises automatically aligning the prosthesis to place at least a portion of the longitudinal support member at least approximately at the superior longitudinal surface line of the curved aorta while traversing the curved portion of the aorta.
14. The method according to claim 1, which further comprises pre-curving the prosthesis in a shape at least approximately corresponding to the curved shape of the curved implantation site.
15. The method according to claim 5, which further comprises pre-forming the longitudinal support member in a curved shape and attaching the curved longitudinal support member to the prosthesis.
16. The method according to claim 5, which further comprises pre-forming the longitudinal support member in a curved shape substantially corresponding to the curved portion of the aorta.
17. The method according to claim 5, which further comprises pre-forming the longitudinal support member in a curved shape substantially corresponding to the curved portion of the aorta in which the prosthesis is to be implanted.
18. The method according to claim 1, which further comprises:
viewing the curve of the vessel in which the prosthesis is to be implanted; and
subsequently forming the curved shape of the curved distal portion to correspond to the curve of the vessel viewed.
19. The method according to claim 18, which further comprises providing a curving device and forming the curved shape of the curved distal portion with the curving device to correspond to the curve of the vessel viewed.
20. The method according to claim 18, which further comprises providing a set of curving devices having differently curved shapes and forming the curved shape of the curved distal portion with at least one of the curing devices to at least approximately correspond to the curve of the vessel viewed.
21. The method according to claim 1, which further comprises connecting the guidewire lumen to the prosthesis delivery system to minimize a restoring force when the guidewire lumen is rotated along its longitudinal axis.
22. The method according to claim 1, which further comprises freely rotatably connecting the guidewire lumen to the prosthesis delivery system to minimize a restoring force when the guidewire lumen is rotated along its longitudinal axis.
22. (canceled)
23. The method according to claim 1, which further comprises rotationally fixedly connecting the guidewire lumen to the distal delivery assembly as the guidewire lumen and the distal delivery assembly advance to the implementation site.
24. The method according to claim 1, which further comprises forming the guidewire lumen from a material permitting ends thereof to be twisted with respect to one another into an at least one-hundred eighty degree axially rotated orientation without deforming the guidewire lumen.
25. The method according to claim 1, which further comprises forming the guidewire lumen from a material permitting ends thereof to be twisted with respect to one another into an at least one-hundred eighty degree axially rotated orientation without producing a restoring force sufficient to harm a vessel wall.
26. The method according to claim 1, which further comprises forming the guidewire lumen from a metal.
27. The method according to claim 1, which further comprises forming the guidewire lumen from stainless steel.
28. The method according to claim 1, wherein the curved distal portion defines a distal portion plane and which further comprises carrying out the further advancing step by further advancing the guidewire lumen with the curved distal portion and the loaded distal delivery assembly into the curved implantation site to at least approximately align the curved distal plane to a superior longitudinal surface line of the curved portion of the vessel.
29. The method according to claim 1, wherein the curved distal portion defines a distal portion plane and which further comprises carrying out the further advancing step by further advancing the guidewire lumen with the curved distal portion and the loaded distal delivery assembly into the curved implantation site to orient the curved distal portion such that the curved distal plane intersects within seventy circumferential degrees of a plane passing through a superior longitudinal surface line of the curved portion of the vessel.
30. The method according to claim 1, which further comprises carrying out the further advancing step by further advancing the guidewire lumen with the curved distal portion and the loaded distal delivery assembly into the curved implantation site to at least approximately orient the prosthesis in an optimal implantation position.
31. The method according to claim 5, wherein the curved distal portion defines a distal portion plane and which further comprises carrying out the loading step by substantially aligning a plane passing through a proximal end of the curved longitudinal support member between thirty-five and fifty-five degrees around the circumference of the prosthesis to the distal portion plane.
32. The method according to claim 5, wherein the curved distal portion defines a distal portion plane and which further comprises carrying out the loading step by substantially aligning a plane passing through a proximal end of the curved longitudinal support member at approximately forty-five degrees around the circumference of the prosthesis to the distal portion plane.
33. The method according to claim 5, wherein a superior longitudinal surface line of the curved portion of the vessel in which the prosthesis is to be implanted defines a superior plane and which further comprises carrying out the further advancing step by further advancing the guidewire lumen with the curved distal portion and the loaded distal delivery assembly into the curved implantation site to at least approximately intersect the superior plane with a proximal half of the curved longitudinal support member.
34. A method for automatic endovascular alignment of a prosthesis in a curved implantation site of a vessel, which comprises:
advancing a guidewire lumen having a curved distal portion along a guidewire and into the curved implantation site to at least approximately align the curved distal portion to a curve of the curved implantation site.
35-75. (canceled)
76. A method for automatic endovascular alignment of a prosthesis, loaded in a delivery assembly, in a curved implantation site of a vessel having positioned therein a guidewire extending at least into the implantation site, which comprises:
providing a prosthesis delivery system with a longitudinally movable guidewire lumen having a curved distal portion;
threading the guidewire lumen over the guidewire along with the loaded distal delivery assembly to advance the guidewire lumen and the loaded distal delivery assembly towards the implementation site; and
advancing the curved distal portion and the loaded distal delivery assembly into the curved implantation site to at least approximately align the curved distal portion to a curve of the curved implantation site and, thereby, orient the prosthesis.
77. The method according to claim 1, which further comprises connecting the guidewire lumen to the prosthesis delivery system at a proximal-most area of the guidewire lumen to minimize a restoring force when the guidewire lumen is rotated along its longitudinal axis.

1. A viewing angle characteristic improving method in a liquid crystal display device including a liquid crystal panel having three color filters of red, green, and blue which are different from one another in thickness, the viewing angle characteristic improving method comprising:
a characteristic acquisition step of acquiring a gradation-transmittance characteristic indicating a relationship between a gradation value and a transmittance regarding each of the three color filters when the liquid crystal panel is viewed from an oblique direction;
a transmittance approximation range defining step of determining a transmittance approximation range, which is a range of the gradation value in which a difference between a maximum value and a minimum value among three transmittances corresponding to each of the gradation values is minute, based on the gradation-transmittance characteristic;
a parameter determining step of determining a white balance parameter for adjusting white balance of the liquid crystal panel so as to correspond to each of the gradation values for each of red, green, and blue colors; and
a white balance adjustment step of correcting the gradation value of each of the colors based on an input image signal supplied to the liquid crystal display device, by using the white balance parameter determined in the parameter decision step,
wherein in the parameter determining step, the white balance parameter is determined so that the correction of the gradation value in the white balance adjustment step causes the gradation value within the transmittance approximation range to be a maximum gradation value of each of the colors.
2. The viewing angle characteristic improving method according to claim 1, wherein
in the characteristic acquisition step, a plurality of gradation-transmittance characteristics corresponding to oblique views from a plurality of directions are acquired, and
when the transmittance approximation range determined based on each of the gradation-transmittance characteristics is defined as a single direction transmittance approximation range, in the transmittance approximation range defining step, an overlap of the plurality of single direction transmittance approximation ranges determined based on the plurality of gradation-transmittance characteristics is defined as the transmittance approximation range to be referred to in the parameter determining step.
3. The viewing angle characteristic improving method according to claim 1, wherein in the transmittance approximation range defining step, a width of the transmittance to be defined as the transmittance approximation range is gradually increased until a combination of the gradation values of red, green, and blue that enables a target color temperature to be realized in front view comes to exist within the transmittance approximation range.
4. The viewing angle characteristic improving method according to claim 3, wherein
the transmittance approximation range defining step includes:
an initial step of setting a variable indicating the width of the transmittance to a predetermined initial value;
a candidate range determining step of determining a candidate range, based on the gradation-transmittance characteristic, the candidate range being a range of the gradation value in which the difference between the maximum value and the minimum value among the three transmittances corresponding to each of the gradation values is not more than a value of the variable;
a determination step of determining whether or not the combination of the gradation values of red, green, and blue that enables the target color temperature to be realized in front view exists within the candidate range;
a variable value increase step of increasing the value of the variable in accordance with a predetermined rule, when a negative determination is made in the determination step; and
a range decision step of defining the candidate range as the transmittance approximation range to be referred to in the parameter determining step, when affirmative determination is made in the determination step,
wherein in first round of the candidate range determining step, the candidate range is determined based on the variable set in the initial step, and
in second or later round of the candidate range determining step, the candidate range is determined based on the variable whose value is increased in the variable value increase step.
5. The viewing angle characteristic improving method according to claim 1, wherein in the transmittance approximation range defining step, a range of the gradation value in which the difference between the maximum value and the minimum value among the three transmittances corresponding to each of the gradation values is not more than 5% is defined as the transmittance approximation range.
6. A liquid crystal display device which includes a liquid crystal panel having three color filters of red, green, and blue which are different from one another in thickness, and displays an image in the liquid crystal panel based on an input image signal indicating a gradation value of each of the colors, the liquid crystal display device comprising:
a white balance adjustment unit that corrects the gradation value of each of the colors based on the input image signal, by using a white balance parameter which is determined so as to correspond to each of the gradation values for each of the colors; and
a panel drive unit that drives the liquid crystal panel based on gradation values after correction by the white balance adjustment unit,
wherein the white balance parameter is determined for each of the colors of red, green and blue, based on a gradation-transmittance characteristic so that a maximum gradation value of each of the colors after the correction by the white balance adjustment unit is set to the gradation value within a transmittance approximation range, the gradation-transmittance characteristic indicating a relationship between a gradation value and a transmittance regarding each of the three color filters when the liquid crystal panel is viewed from an oblique direction, and the transmittance approximation range being a range of the gradation value in which a difference between a maximum value and a minimum value among three transmittances corresponding to each of the gradation values is minute.
7. The liquid crystal display device according to claim 6, wherein the transmittance approximation range is set to a range of the gradation value in which the difference between the maximum value and the minimum value among the three transmittances corresponding to each of the gradation values is not more than 5%, based on the gradation-transmittance characteristic.
8. The liquid crystal display device according to claim 6, wherein
the liquid crystal panel enables gradation display of 256 gradations, and
a range of the gradation value of 170 to 200 is defined as the transmittance approximation range.

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 method for establishing a multimedia format for communicating data between a first node and the second node, the method comprising:
receiving first node format constraint data;
receiving second node format constraint data; and
negotiating any unresolved format constraints between the first node format constraint data and the second node format constraint data.
2. The method of claim 1, wherein at least one of the first node format constraint data and the second node format constraint data comprises at least one parameter comprising a range of acceptable values.
3. The method of claim 2, wherein negotiating any unresolved format constraints further comprises identifying a parameter within the range of acceptable values.
4. The method of claim 1, further comprising:
based on the first node format constraint data and the second node format constraint data, selecting a filter node for communicating the data between the first node and the second node.
5. The method of claim 4, wherein the filter node transforms the data.
6. The method of claim 1, further comprising generating a graph of at least one connection between the first node and the second node.
7. The method of claim 6, wherein the generated graph further comprises at least one filter node connected between the first node and the second node.
8. The method of claim 6, wherein the negotiation of unresolved format constraints occurs according to the generated graph.
9. The method of claim 6, wherein the step of negotiating unresolved format constraints occurs for all nodes in the graph in a single transaction after all nodes have been connected to the graph.
10. The method of claim 1, wherein a framework that communicates with the first node and the second node performs the steps of receiving the first node format constraint data, receiving the second node format constraint data and negotiating any unresolved format constraints.
11. The method of claim 10, wherein negotiating the unresolved format constraints comprises selecting one of the range of compatible values according to the published format constraints of the first node and second node for the communication of data.
12. The method of claim 11, wherein negotiating the unresolved format constraints comprises selecting a first value for the first node from the range of acceptable values that is compatible with a second value from the range of acceptable values for the second node.
13. The method of claim 12, wherein negotiating any unresolved format constraints comprises selecting a set of format data that is compatible with the received first node format constraint data, the second node format constraint data any logical expressions associated with the first node format constraint data or the second node format constraint data.
14. The method of claim 1, wherein the format constraint data comprises at least one logical expression in disjunctive normal form.
15. The method of claim 1, wherein negotiating any unresolved format constraints further comprises identifying alternate contents or an alternate transform content formats to optimize content for the second node.
16. The method of claim 15, wherein a remote server or proxy server is used to identify the alternate content or alternate transform content formats.
17. The method of claim 1, wherein a proxy server performs the step of negotiating remove from a computing device associated with the first node and the second node.
18. The method of claim 1, wherein the first node format data comprises at least one wild card parameter.
19. The method of claim 18, wherein the second node format constraint data comprises at least one of wild card parameter, and wherein negotiating any unresolved format constraints comprises selecting any value for the wild-card parameter.
20. The method of claim 1, wherein the first node is a source node and the second node is a destination node.
21. The method of claim 1, wherein the first node is a source node and the second node is a filter node.
22. A system that establishes a multimedia format for communicating data between a first node and a second node, the system comprising:
a module configured to receive a first node format constraint data;
a module configured to receive a second node format constraint data; and
a module configured to negotiate any unresolved format constraints between the first node format constraint data and the second node format constraint data.
23. A system that establishes a multimedia format for communicating data between a first node and a second node, the system comprising:
means for receiving a first node format constraint data;
means for receiving second node format constraint data; and
means for negotiating any unresolved format constraints between the first node format constraint data and the second node constraint format data.
24. A computer readable medium storing instructions for controlling a computing device to establish a format for communicating data between a first node and a second node, the instructions comprising:
receiving first node format constraint data;
identifying a second node having a second node format constraint data; and
negotiating any unresolved format constraints between the first node format constraint data and the second node format constraint data.
25. A method of generating a graph for use in communicating data from a source node to a destination node, the graph comprising at least one node, the method comprising:
(1) requesting a connection of a new node to the graph by propagating a format constraint for the new node to the at least one node in the graph;
(2) if the propagation fails, then denying the request for connection; and
(3) if the propagation is successful, then connecting the new node to the graph.
26. The method of claim 25, wherein a successful propagation further comprises determining whether the format constraint for the new node is compatible with format constraints for the at least one node in the graph.
27. The method of claim 25, further comprising repeating steps (1)-(3) for each new node to be connected to the graph.
28. The method of claim 27, wherein after each new node is connected to the graph, the method further comprises:
resolving all format constraints for each node in the graph.
29. A system for communicating data from a source node to a destination node using a graph, the graph comprising at least one node, the system comprising:
(1) means for requesting a connection of a new node to the graph by propagating a format constraint for the new node to the at least one node in the graph;
(2) means for denying the request for connection if the propagation fails; and
(3) means for connecting the new node to the graph if the propagation is successful.