1. A method for unsupervised classification of histological images of prostatic tissue, comprising the steps of: providing histological image data obtained from a slide simultaneously co-stained with NIR fluorescent and Hematoxylin-and-Eosin (H&E) stains; segmenting prostate gland units in the image data; forming feature vectors by computing discriminating attributes of the segmented gland units; and using said feature vectors to train a multi-class classifier within a Bayesian framework, wherein said classifier is arranged to classify prostatic tissue into benign, prostatic intraepithelial neoplasia (PIN), and Gleason scale adenocarcinoma grades 1 to 5 categories and to use Bayesian posterior probabilities to determine a strength of a diagnosis, wherein a borderline prognosis between two categories is provided to a second phase classifier using a classification model whose parameters are tuned to the two categories of the borderline prognosis.