1. A method of classifying, with respect to a predefined disease, an optical tomographic image of a living sample tissue, comprising: scanning light into tissue samples and capturing trans-illumination data from the tissue samples; using optical tomographic imaging to generate a first set of images of the tissue samples from the trans-illumination data, including diseased tissue samples having the predefined disease and healthy tissue samples not having the predefined disease; extracting, from the first set of images, a plurality of features selected from the group consisting of volumetric features and projection dependent features, the features representing optical properties of a sample tissue; statistically analyzing each of the features and selecting responsively to a result of the statistically analyzing, a subset of the features that provides greater predictive accuracy as to the presence of the disease than other features when applied to a predefined classifier; scanning light into further tissue samples and capturing further trans-illumination data from the further tissue samples; using optical tomographic imaging to generate a second set of images of the further tissue samples from the further trans-illumination data; and using the subset of features with the predefined classifier to classify the further tissue samples as having the having the predefined disease or not having the predefined disease based on the second set of images, wherein the images in the first and second sets include, for each tissue sample, multiple structured images combined into a planar image from which the at least one of the features is extracted.