Recent advances in high-resolution fluorescence microscopy have enabled the systematic
study of morphological changes in large populations of cells induced by chemical and
genetic perturbations, facilitating the discovery of signaling pathways underlying
diseases and the development of new pharmacological treatments. In these studies, though,
due to the complexity of the data, quantification and analysis of morphological features
are for the vast majority handled manually, slowing significantly data processing and
limiting often the information gained to a descriptive level. Thus, there is an urgent
need for developing highly efficient automated analysis and processing tools for
fluorescent images. In this paper, we present the application of a method based on the
shearlet representation for confocal image analysis of neurons. The shearlet
representation is a newly emerged method designed to combine multiscale data analysis with
superior directional sensitivity, making this approach particularly effective for the
representation of objects defined over a wide range of scales and with highly anisotropic
features. Here, we apply the shearlet representation to problems of soma detection of
neurons in culture and extraction of geometrical features of neuronal processes in brain
tissue, and propose it as a new framework for large-scale fluorescent image analysis of
biomedical data.