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, wherein said classifier is adapted to classify prostatic tissue into benign, prostatic intraepithelial neoplasia (PIN), and Gleason scale adenocarcinoma grades 1 to 5 categories.