US 9,811,904 B2
Method and system for determining a phenotype of a neoplasm in a human or animal body
Philippe Lambin, Maastricht (NL); and Hugo Johannes Wilhelmus Louis Aerts, Maastricht (NL)
Assigned to STICHTING MAASTRICHT RADIATION ONCOLOGY “MAASTRO-CLINIC”, Maastricht (NL)
Appl. No. 14/783,974
Filed by Stichting Maastricht Radiation Oncology “Maastro-Clinic”, Maastricht (NL)
PCT Filed Apr. 17, 2014, PCT No. PCT/NL2014/050248
§ 371(c)(1), (2) Date Oct. 12, 2015,
PCT Pub. No. WO2014/171830, PCT Pub. Date Oct. 23, 2014.
Claims priority of application No. 13164418 (EP), filed on Apr. 19, 2013.
Prior Publication US 2016/0078613 A1, Mar. 17, 2016
Int. Cl. G06K 9/00 (2006.01); G06T 7/00 (2017.01)
CPC G06T 7/0012 (2013.01) [G06K 9/0014 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/30064 (2013.01); G06T 2207/30096 (2013.01)] 18 Claims
OG exemplary drawing
 
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.