Evaluation of features for automatic detection of cell nuclei in fluorescence microscopy images

The problem of detecting cell nuclei in fluorescence images may be faced by means of a segmentation step, to get the neighbourhood of candidate nuclei, followed by a binary classification step. Important for the latter step is the choice of the descriptors (features) to be extracted from the neighbourhood and used by the classifier. In the present paper, based on a large set of manually labelled samples, we evaluate several of such descriptors combined with some common type of support vector machines. We show that equipping the detection algorithm with the best combination of features/classifier leads to a performance comparable to human labelling by experts.

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