A method for identifying induced pluripotent stem cells from an image of cells in a non- destructive and non-invasive manner, comprising: obtaining an image of one or more cells including a mix of at least feeder cells and induced pluripotent stem cells, wherein the induced pluripotent stem cells are dedifferentiated from the feeder cells; representing the image as a multiplicity of pixels; dividing the multiplicity of pixels into a plurality of windows; extracting one or more image features from each of the plurality of windows, the one or more image features each including a texture corresponding to a morphological feature of the one or more cells; comparing the one or more image features with image features derived from one or more pluripotent stem cells, by applying one or more statistical comparison methods to compare the image features, wherein the one or more statistical methods is a comparison of probability density functions (pdfs); classifying the plurality of windows into a set of textural classes using a k-nearest neighbor classifier by computing a dissimilarity between an unknown window to each library model compiled from pluripotent and differentiated images and k models with the smallest dissimilarity voting with a class with most votes winning, wherein the k models are only allowed to vote if the dissimilarity is below a ceiling value and an end result is only accepted if at least k n ≤ k votes concur, wherein the dissimilarity is calculated using a pdf estimator and quantified using a Kullback-Leibler divergence; identifying, using spatial reasoning rules, textural border windows for refined classification of the plurality of windows, the spatial reasoning rules comprising: selecting windows failing the k-nearest neighbor test, and selecting windows classified as differentiated that borders upon a window of a different class; subdividing the textural border windows; re-iterating the classification, the identification, and the subdivision of the identified textural border windows until characteristic features are on an order of window size; and identifying induced pluripotent stem cells within the mix of cells in the subdivided textural border windows. obtaining an image of one or more cells including a mix of at least feeder cells and induced pluripotent stem cells, wherein the induced pluripotent stem cells are dedifferentiated from the feeder cells; representing the image as a multiplicity of pixels; dividing the multiplicity of pixels into a plurality of windows; extracting one or more image features from each of the plurality of windows, the one or more image features each including a texture corresponding to a morphological feature of the one or more cells; comparing the one or more image features with image features derived from one or more pluripotent stem cells, by applying one or more statistical comparison methods to compare the image features, wherein the one or more statistical methods is a comparison of probability density functions (pdfs); classifying the plurality of windows into a set of textural classes using a k-nearest neighbor classifier by computing a dissimilarity between an unknown window to each library model compiled from pluripotent and differentiated images and k models with the smallest dissimilarity voting with a class with most votes winning, wherein the k models are only allowed to vote if the dissimilarity is below a ceiling value and an end result is only accepted if at least k n ≤ k votes concur, wherein the dissimilarity is calculated using a pdf estimator and quantified using a Kullback-Leibler divergence; identifying, using spatial reasoning rules, textural border windows for refined classification of the plurality of windows, the spatial reasoning rules comprising: selecting windows failing the k-nearest neighbor test, and selecting windows classified as differentiated that borders upon a window of a different class; selecting windows failing the k-nearest neighbor test, and selecting windows classified as differentiated that borders upon a window of a different class; subdividing the textural border windows; re-iterating the classification, the identification, and the subdivision of the identified textural border windows until characteristic features are on an order of window size; and identifying induced pluripotent stem cells within the mix of cells in the subdivided textural border windows.