ABSTRACT
The introduction of multispectral imaging in pathology problems such as the identification of prostatic cancer is recent. Unlike conventional RGB color space, it allows the acquisition of a large number of spectral bands within the visible spectrum. The major problem arising in using multispectral data is high-dimensional feature vector size. The number of training samples used to design the classifier is small relative to the number of features. For such a high dimensionality problem, pattern recognition techniques suffer from the wellknown curse of dimensionality. The aim of this chapter is to discuss and compare two tabu search-based computational intelligence algorithms proposed recently by authors for the detection and classification of prostatic tissues using multispectral imagery.
