A NOVEL FEATURE DESCRIPTOR BASED ON MICROSCOPY IMAGE STATISTICS

In this paper, we propose a novel feature description algo-rithm based on image statistics. The pipeline first performs independent component analysis on training image patches to obtain basis vectors (filters) for a lower dimensional represen-tation. 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 identi-fication 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 differ-ent microscopy modalities. Index Terms — local image descriptor, pixel labeling, cell detection, phase contrast imaging, electron microscopy, mito-chondria, tumor, cell co-culture. 1