1. A method of identifying an object of interest in digital images, the method comprising: a. obtaining first samples of an intensity distribution of one or more objects of interest in one or more of the digital images based upon one or more wavelength bands; b. obtaining second samples of an intensity distribution of confounder objects in one or more of the digital images, at a predetermined frequency; c. transforming the first and second samples into an appropriate first space; d. performing dimensionality factor reduction on the transformed first and second samples, whereby the dimensionality factor reduction of the transformed first and second samples generates an object detector; e. transforming one or more of the digital images into the first space; f. performing dimensionality factor reduction on the transformed digital images, whereby the dimensionality factor reduction of the transformed digital images generates one or more reduced images; g. classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, wherein the classification comprises: locating one or more K samples that minimize a distance to pixels in a pre-defined neighborhood; and classifying one or more pixels as one of abnormal and normal, using the distance to the K samples and a label associated with the K samples, wherein a lesion likelihood index L p for a pixel p of neighborhood N p is obtained from the K nearest samples (S k ) k=1. .. K , with labels (l k ) k=1. .. K equal to 1 for lesions and −1 for negative lesion confounders, by the following formula L p =Σ k=1 K l k exp(−∥N p −S k ∥) and used to automatically classify the pixel p as abnormal or normal; and h. identifying one or more objects of interest in the reduced digital images from the classified pixels.