An image processing system, comprising: an input port (IN) for receiving two or more input images (A, B) acquired of an object (OB), wherein respective contrast in at least two of said images encodes information on different physical properties of the object, said images being converted from a signal detected at a detector (D) of an imaging apparatus (IM), wherein the signals from the detector (D) are a combination of a true underlying signal (s) and a noise contribution (n); a differentiator (DIFF) configured to form respective differences from pairs of image points (a 1 , a 2 , b 1 , b 2 ) from respective input images, wherein each pair is from a respective one of the input images (A,B), wherein the image pairs are spatially corresponding image samples, wherein samples are taken at the same respective locations in the input images (A,B), wherein the image signals in the respective image pairs comprise a noise contribution realization (n 1 , n 2 , n 3 , n 4 ); an edge evaluator (EV) configured to compute, based on said differences, an edge score for at least one of said pairs of image points, wherein said edge score is a measure for the presence of an edge between the image points in at least one of said image pairs, said score is based on a measure that is a function of a likelihood function, said likelihood function is based on a joint probability density (p(n 1 , n 2 , n 3 , n 4 )) of the noise contribution realization (n 1 , n 2 , n 3 , n 4 ) of the image signals in the respective image pairs, wherein the noise is Gaussian multi-variate; and an output port (OUT) for outputting said score. an input port (IN) for receiving two or more input images (A, B) acquired of an object (OB), wherein respective contrast in at least two of said images encodes information on different physical properties of the object, said images being converted from a signal detected at a detector (D) of an imaging apparatus (IM), wherein the signals from the detector (D) are a combination of a true underlying signal (s) and a noise contribution (n); a differentiator (DIFF) configured to form respective differences from pairs of image points (a 1 , a 2 , b 1 , b 2 ) from respective input images, wherein each pair is from a respective one of the input images (A,B), wherein the image pairs are spatially corresponding image samples, wherein samples are taken at the same respective locations in the input images (A,B), wherein the image signals in the respective image pairs comprise a noise contribution realization (n 1 , n 2 , n 3 , n 4 ); an edge evaluator (EV) configured to compute, based on said differences, an edge score for at least one of said pairs of image points, wherein said edge score is a measure for the presence of an edge between the image points in at least one of said image pairs, said score is based on a measure that is a function of a likelihood function, said likelihood function is based on a joint probability density (p(n 1 , n 2 , n 3 , n 4 )) of the noise contribution realization (n 1 , n 2 , n 3 , n 4 ) of the image signals in the respective image pairs, wherein the noise is Gaussian multi-variate; and an output port (OUT) for outputting said score. <EOS>