A novel prostate cancer classification technique using intermediate memory tabu search

Authors:
Muhammad Atif Tahir;Ahmed Bouridane;Fatih Kurugollu;Abbes Amira
Affiliations:
School of Computer Science, Queen's University of Belfast, Belfast, Northern Ireland, UK;School of Computer Science, Queen's University of Belfast, Belfast, Northern Ireland, UK;School of Computer Science, Queen's University of Belfast, Belfast, Northern Ireland, UK;School of Computer Science, Queen's University of Belfast, Belfast, Northern Ireland, UK
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2005

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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.