A novel feature descriptor based on microscopy image statistics

In this paper, we propose a novel feature description algorithm based on image statistics. The pipeline first performs independent component analysis on training image patches to obtain basis vectors (filters) for a lower dimensional representation. Then for a given image, a set of filter responses at each pixel is computed. Finally, a histogram representation, which considers the signs and magnitudes of the responses as well as the number of filters, is applied on local image patches. We propose to apply this idea to a microscopy image pixel identification system based on a learning framework. Experimental results show that the proposed algorithm performs better than the state-of-the-art descriptors in biomedical images of different microscopy modalities.