Abstract: A pixel image is received and bounded contours are identified. Bounded contours are categorized based on certain pixel statistics, both comparative and with respect to given thresholds and other criteria, into one of a plurality of given categories. Image objects within certain categories are further characterized with respect to a certain estimation of area and symmetry.

Abstract: A gradient is calculated for a pixel image representing a cancer area, the gradient having magnitude and direction. Local extrema of the gradients are identified, remaining pixels are zeroed. Significant gradients among the local extrema are identified, based on thresholds obtained through training. Probability distributions are calculated for the local extrema magnitudes and the significant gradient magnitudes, to form a feature vector. The feature vector is classified using a Maximum Likelihood Estimate classifier constructed from large training sets.

Abstract: A pixel of an image is classified between a first kind and a second kind by centering a sample mask on the pixel and applying each of a population of R given basis functions to the mask pixels to generate, for each basis function, a bucket of values. A probability density function is estimated for each of the bucket of values. Each of the R probability density functions is transformed to a single valued result, to generate an R-dimensional sample classification vector. The R-dimensional sample classification vector is classified against a R-dimensional first centroid vector and a R-dimensional second centroid vector, each of centroid vectors constructed in a previous training of applying the same population of R given basis functions to pixels known as being the first kind and to pixels known as being the second kind. Optionally, pixels may be conditionally classified and then finally classified based on subsequent classification of neighbor pixels.