A system comprising means arranged for performing 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, the features representing optical properties of a sample tissue, wherein the plurality of features includes a volumetric feature and a projection dependent feature wherein projection dependent features are features that are extracted from projections obtained by projecting said optical properties into various two-dimensional spaces and volumetric features comprise (i) features extracted from said projections that include the average maximum, the average minimum, the mean, mode, median and variance between non-extreme elements of the optical property, and ratio between maximum and minimum and (ii) additional features extracted from projections including (a) projections obtained by summation of all sagittal, coronal and transverse slices, respectively, (b) projections quantifying the variation between all sagittal, coronal and transverse slices, respectively, (c) projections defining the geometrically dominant sagittal, coronal and transverse slice, said additional features being extracted by parameterizing the distribution of the optical coefficients; 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 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 at least one of the plurality of features is extracted. 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, the features representing optical properties of a sample tissue, wherein the plurality of features includes a volumetric feature and a projection dependent feature wherein projection dependent features are features that are extracted from projections obtained by projecting said optical properties into various two-dimensional spaces and volumetric features comprise (i) features extracted from said projections that include the average maximum, the average minimum, the mean, mode, median and variance between non-extreme elements of the optical property, and ratio between maximum and minimum and (ii) additional features extracted from projections including (a) projections obtained by summation of all sagittal, coronal and transverse slices, respectively, (b) projections quantifying the variation between all sagittal, coronal and transverse slices, respectively, (c) projections defining the geometrically dominant sagittal, coronal and transverse slice, said additional features being extracted by parameterizing the distribution of the optical coefficients; 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 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 at least one of the plurality of features is extracted.