Open Access

A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search

  • Muhammad Atif Tahir1Email author,
  • Ahmed Bouridane1,
  • Fatih Kurugollu1 and
  • Abbes Amira1
EURASIP Journal on Advances in Signal Processing20052005:906054

https://doi.org/10.1155/ASP.2005.2241

Received: 31 December 2003

Published: 25 August 2005

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. This results in a feature vector of size greater than 100. For such a high dimensionality, pattern recognition techniques suffer from the well-known curse of dimensionality problem. The two well-known techniques to solve this problem are feature extraction and feature selection. In this paper, a novel feature selection technique using tabu search with an intermediate-term memory is proposed. The cost of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN) classifier. The experiments have been carried out on the prostate cancer textured multispectral images and the results have been compared with a reported classical feature extraction technique. The results have indicated a significant boost in the performance both in terms of minimizing features and maximizing classification accuracy.

Keywords and phrases

feature selection dimensionality reduction tabu search 1-NN classifier prostate cancer classification

Authors’ Affiliations

(1)
School of Computer Science, Queen's University of Belfast

Copyright

© Tahir et al. 2005