Method and system for determining a phenotype of a neoplasm in a human or animal body
First Claim
1. An image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, comprising the steps of:
- receiving, by a processing unit, image data of the neoplasm; and
deriving, by the processing unit, a plurality of image feature parameter values from the image data, said image parameter values relating to image features associated with the neoplasm; and
deriving, by said processing unit using a signature model, one or more neoplasm signature model values associated with the neoplasm from said image feature parameter values, wherein said signature model includes a functional relation between or characteristic values of said image feature parameter values for deriving said neoplasm signature model values therefrom;
wherein the image feature parameter values are indicative of image feature parameters, wherein the signature model includes at least all of the image feature parameters from a group comprising;
gray-level non-uniformity, and wavelet high-low-high gray-level run-length gray-level non-uniformity.
2 Assignments
0 Petitions
Accused Products
Abstract
The present invention relates to a decision support system and an image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, comprising the steps of: receiving, by a processing unit, image data of the neoplasm; and deriving, by the processing unit, a plurality of image feature parameter values from the image data, said image parameter values relating to image features associated with the neoplasm; and deriving, by said processing unit using a signature model, one or more neoplasm signature model values associated with the neoplasm from said image feature parameter values, wherein said signature model includes a functional relation between or characteristic values of said image feature parameter values for deriving said neoplasm signature model values.
-
Citations
18 Claims
-
1. An image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, comprising the steps of:
-
receiving, by a processing unit, image data of the neoplasm; and deriving, by the processing unit, a plurality of image feature parameter values from the image data, said image parameter values relating to image features associated with the neoplasm; and deriving, by said processing unit using a signature model, one or more neoplasm signature model values associated with the neoplasm from said image feature parameter values, wherein said signature model includes a functional relation between or characteristic values of said image feature parameter values for deriving said neoplasm signature model values therefrom; wherein the image feature parameter values are indicative of image feature parameters, wherein the signature model includes at least all of the image feature parameters from a group comprising;
gray-level non-uniformity, and wavelet high-low-high gray-level run-length gray-level non-uniformity. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18)
-
-
12. A decision support system arranged for performing an image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, said system comprising an input connected to a processing unit for receiving by the processing unit image data of the neoplasm;
- wherein the processing unit is further arranged for deriving a plurality of image feature parameter values from the received image data, said image parameter values relating to image features associated with the neoplasm, wherein the processing unit is connected to a memory for obtaining therefrom at least one signature model comprising one or more signature selector values associated with the image features, wherein the signature selector values indicate whether the associated image features are comprised by the signature model, and wherein the processing unit is arranged for multiplying for the at least one signature model the image feature parameter values with the associated signature selector values for obtaining one or more neoplasm signature model values associated with the neoplasm;
wherein the image feature parameter values are indicative of image feature parameters, wherein the signature model includes at least all of the image feature parameters from a group comprising;
statistics energy, shape compactness, gray-level non-uniformity, wavelet high-low-high gray-level run-length gray-level non-uniformity. - View Dependent Claims (13, 14)
- wherein the processing unit is further arranged for deriving a plurality of image feature parameter values from the received image data, said image parameter values relating to image features associated with the neoplasm, wherein the processing unit is connected to a memory for obtaining therefrom at least one signature model comprising one or more signature selector values associated with the image features, wherein the signature selector values indicate whether the associated image features are comprised by the signature model, and wherein the processing unit is arranged for multiplying for the at least one signature model the image feature parameter values with the associated signature selector values for obtaining one or more neoplasm signature model values associated with the neoplasm;
-
15. A non-transistory computer-readable medium comprising computer-executable instructions which, when run on a computer, are arranged for performing an image analysis method for providing information for enabling determination of a phenotype of a neoplasm in a human or animal body for enabling prognostication, the method comprising the steps of:
-
receiving, by a processing unit, the image data of the neoplasm; and deriving, by the processing unit, a plurality of image feature parameter values from the image data, said image parameter values relating to image features associated with the neoplasm; and deriving, by said processing unit using a signature model, one or more neoplasm signature model values associated with the neoplasm from said image feature parameter values, wherein said signature model includes a functional relation between or characteristic values of said image feature parameter values deriving said neoplasm signature model values therefrom, wherein the image feature parameter values are indicative of image feature parameters, and wherein the signature model includes at least all of the image feature parameters from a group comprising; gray-level non-uniformity, and wavelet high-low-high gray-level run-length gray-level non-uniformity.
-
Specification