17. The apparatus of claim 16 , where the learning logic extracts a set of positive salient feature sample pixels from a magnetic resonance (MR) image of a region of tissue, extracts a set of negative non-salient feature pixels from the image, extracts multiscale local binary pattern (MsLBP) codes from the set of positive sample pixels and the set of negative sample pixels, and computes a weighted vector by discriminatively training a vector that weighs the Hamming value of a pixel at a plurality of scales.
17. The apparatus of claim 16 , where the learning logic extracts a set of positive salient feature sample pixels from a magnetic resonance (MR) image of a region of tissue, extracts a set of negative non-salient feature pixels from the image, extracts multiscale local binary pattern (MsLBP) codes from the set of positive sample pixels and the set of negative sample pixels, and computes a weighted vector by discriminatively training a vector that weighs the Hamming value of a pixel at a plurality of scales. 18. The apparatus of claim 17 where the salient feature detection logic iteratively selects a template pixel from the image, searches over the image to match the local binary pattern (LBP) of a pixel in the image with the LBP of the template pixel at a plurality of scales, finds the Hamming distance for the pixel at a plurality of scales, and computes a weighted sum as a function of the weighted vector and a column vector of the Hamming distance.