Invariant texture classification for biomedical cell specimens via non-linear polar map filtering

A novel texture-based classification scheme for cell specimens that is robust over a range of orientation, scale and contrast values is proposed. We achieve this robustness by first segmenting the cell specimens and for each specimen, we find the largest ellipse that can be contained within it, and from this, we then construct an orientation and scale-invariant polar map. Non-linear filtering by normalized cross-correlation is then performed on the polar map to obtain contrast-invariant similarity maps. Local and global energy measures are finally extracted from these maps and classified using a support vector machine. Experimental results show that the proposed method achieves an average accuracy of about 97% in classifying six species of pollen, fungal and fern spores. In addition, every invariant property was validated through a series of experiments. Unlike conventional wavelet decomposition, Laws filtering and co-occurrence methods, our method shows a consistently high classification accuracy for all classes of cell specimens in an airspora dataset.