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A quadratic classifier based on multispectral texture features for prostate cancer diagnosis

This paper is concerned with the development of an automatic classification system for use in prostate cancer diagnosis. The system aims to detect and classify nuclei textures captured from microscopic samples taken from needle biopsies. The main contribution here is that the analysis is carried out over thirty-three spectral bands instead of using the conventional grey scale or RGB colour spaces. A set of texture and morphological features has been computed for all these spectral bands for use in the discrimination phase. The large vector size has then been reduced to a manageable size by using a principal component analysis. Classification tests have been carried out using quadratic discriminant analysis and have shown that multispectral analysis significantly improves the overall classification performances when compared with the case where multispectral features are not considered.