13. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a computer cause the computer to perform a method, the method comprising: accessing an input image, where the input image is a digitized image comprising a set of pixels and intensity information about the set of pixels; labelling a non-empty subset of pixels in the input image as a positive set; labelling a non-empty subset of pixels in the input image as a negative set, where the negative set does not include the positive set; computing a first sum of positive local binary pattern (LBP) descriptors as a function of the positive set; computing a second sum of negative and positive LBP descriptors as a function of the negative set and the positive set; computing a weighted vector from the first sum of positive LBP descriptors and the second sum of negative and positive LBP descriptors; extracting a multiscale LBP at a co-ordinate from the input image at a plurality of scales; finding the Hamming distance of the multiscale LBP at the plurality of scales; computing a weighted sum as a function of the transpose of the weighted vector and a column vector of the Hamming distance of the multiscale LBP; assigning the weighted sum for the co-ordinate to a statistical probability heatmap of the input image; updating a memory to store the statistical probability heatmap, and controlling a display to display the statistical probability heatmap, where the statistical probability heatmap facilitates distinguishing regions of the input image associated with the positive set and regions of the input image associated with the negative set.
13. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a computer cause the computer to perform a method, the method comprising: accessing an input image, where the input image is a digitized image comprising a set of pixels and intensity information about the set of pixels; labelling a non-empty subset of pixels in the input image as a positive set; labelling a non-empty subset of pixels in the input image as a negative set, where the negative set does not include the positive set; computing a first sum of positive local binary pattern (LBP) descriptors as a function of the positive set; computing a second sum of negative and positive LBP descriptors as a function of the negative set and the positive set; computing a weighted vector from the first sum of positive LBP descriptors and the second sum of negative and positive LBP descriptors; extracting a multiscale LBP at a co-ordinate from the input image at a plurality of scales; finding the Hamming distance of the multiscale LBP at the plurality of scales; computing a weighted sum as a function of the transpose of the weighted vector and a column vector of the Hamming distance of the multiscale LBP; assigning the weighted sum for the co-ordinate to a statistical probability heatmap of the input image; updating a memory to store the statistical probability heatmap, and controlling a display to display the statistical probability heatmap, where the statistical probability heatmap facilitates distinguishing regions of the input image associated with the positive set and regions of the input image associated with the negative set. 14. The non-transitory computer-readable storage medium of claim 13 , where computing a weighted vector w comprises optimizing min w w T (Σ P −αΣ PN )w such that b T w=1 and 0≦w≦1, where b=[1, . . . , 1] T , bεR N×1 , where P is the first sum, where PN is the second sum, where Σ P −αΣ PN is a symmetric matrix, where α is chosen to keep Σ P −αΣ PN positive definite, and where N is a number of operations of varying (p, r), where p is a set of equally spaced pixels on a circle with radius r of the input image.