A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search
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A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search Muhammad Atif Tahir School of Computer Science, Queen’s University of Belfast, Belfast BT7 1NN, Northern Ireland, UK Email: [email protected]
Ahmed Bouridane School of Computer Science, Queen’s University of Belfast, Belfast BT7 1NN, Northern Ireland, UK Email: [email protected]
Fatih Kurugollu School of Computer Science, Queen’s University of Belfast, Belfast BT7 1NN, Northern Ireland, UK Email: [email protected]
Abbes Amira School of Computer Science, Queen’s University of Belfast, Belfast BT7 1NN, Northern Ireland, UK Email: [email protected] Received 31 December 2003; Revised 2 November 2004 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 wellknown 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.
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INTRODUCTION
Prostate cancer has become the second most commonly diagnosed cancer in the male population after lung cancer, with approximately 22 800 new cases diagnosed every year in the UK alone. Currently, prostate needle biopsy remains the only conclusive way to make an accurate diagnosis of prostate cancer [1]. Recently Roula et al. have described a novel approach in which additional spectral data is used for the classification of prostate needle biopsies [2, 3]. The aim of this novel approach is to help pathologists reduce the diagnosis error rate. Instead of analyzing conventional grey scale or RGB color images, spectral bands have been used in the analysis. Results have shown that the multispectral image classification outperforms both RGB and grey-level-
based classification. The following four classes have been discriminated. (i) Stroma: STR (muscular normal tissue). (ii) Benign prostatic hyperplasia: BPH (a benign condition). (iii) Prostatic intraepithelial neoplasia: PIN (a precursor state for cancer). (iv) Prostatic carcinoma: PCa (abnormal tissue development corresponding to cancer). Figure 1 shows samples of
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