A method of visualizing and characterizing two or more features in a first image, comprising: applying a first local micro-contrast convergence algorithm to a first image to produce a second image that is separate and distinct from the first image, wherein the second image includes the features, wherein applying the first local micro-contrast convergence algorithm includes iteratively and sequentially applying each of a plurality of non-linear discontinuous transfer functions to a corresponding image in a sequential set of images including the first image and one or more intermediate images, wherein each subsequent image in the sequential set of images is generated by applying one of the plurality of non-linear discontinuous transfer functions to a previous image in the sequential set of images thereby resulting in generation of the second image to visualize and characterize, in the second image, two or more physical or biological structural visualized and characterized patterns that were present but not characterized in the first image; and wherein local micro-contrast convergence represents a sequence of transfer functions including the plurality of non-linear discontinuous transfer functions, wherein at least one of the plurality of non-linear discontinuous transfer functions includes at least one discontinuity, the method characterized in that: applying the plurality of non-linear discontinuous transfer functions to the first image includes utilizing one or more grayscale or color profile look up tables representative of the non-linear discontinuous transfer functions, wherein a profile look up table is a mathematical expression in a matrix that specifies image input and output values so that localized, self-similar image contrast pixel variables in the first image, have discrete sets of values where the one or more pixels having similar characteristics in the a source image are assigned a single color or luminance value in a resulting output image to thereby cause neighboring pixel groups representative of specific structures to converge to cause relationships among neighboring pixel groups to aggregate into fractal geometry-based color and luminosity patterns, wherein the two or more physical or biological structural visualized and characterized patterns include at least one of: fractal-geometry based tissue feature, a tissue feature differentiation, a boundary of cancerous and benign tissue structures in the second image, and define an internal structure of cancerous and/or benign tissue structures in the second image. applying a first local micro-contrast convergence algorithm to a first image to produce a second image that is separate and distinct from the first image, wherein the second image includes the features, wherein applying the first local micro-contrast convergence algorithm includes iteratively and sequentially applying each of a plurality of non-linear discontinuous transfer functions to a corresponding image in a sequential set of images including the first image and one or more intermediate images, wherein each subsequent image in the sequential set of images is generated by applying one of the plurality of non-linear discontinuous transfer functions to a previous image in the sequential set of images thereby resulting in generation of the second image to visualize and characterize, in the second image, two or more physical or biological structural visualized and characterized patterns that were present but not characterized in the first image; and wherein local micro-contrast convergence represents a sequence of transfer functions including the plurality of non-linear discontinuous transfer functions, wherein at least one of the plurality of non-linear discontinuous transfer functions includes at least one discontinuity, the method characterized in that: applying the plurality of non-linear discontinuous transfer functions to the first image includes utilizing one or more grayscale or color profile look up tables representative of the non-linear discontinuous transfer functions, wherein a profile look up table is a mathematical expression in a matrix that specifies image input and output values so that localized, self-similar image contrast pixel variables in the first image, have discrete sets of values where the one or more pixels having similar characteristics in the a source image are assigned a single color or luminance value in a resulting output image to thereby cause neighboring pixel groups representative of specific structures to converge to cause relationships among neighboring pixel groups to aggregate into fractal geometry-based color and luminosity patterns,