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 well known 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.
Original languageEnglish
Title of host publicationComputational Intelligence in Medical Imaging: Techniques and Applications
EditorsG. Schaefer, A. Hassanien, J. Jiang
PublisherCRC Press
Number of pages27
ISBN (Electronic)978-0429140297
ISBN (Print)13-978-1-4200-6059-1
Publication statusPublished - 2009

ID: 4000841