Multispectral imagery in prostate cancer pathology has progressed greatly in the last 5 years. Unlike conventional RGB colour space, multispectral images allow the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. For such a high dimensionality, pattern recognition techniques suffer from the well-known curse of dimensionality problem along with increase in execution time. This book investigates novel classification algorithms for prostate cancer classification using multispectral images and the suitability of reconfigurable computing to speedup medical image classification problems.