Colour Image in 2d and 3d Microscopy for the Automation of Pollen Rate Measurement

Pollen monitoring is of great importance for the prevention of allergy. As this activity still is largely carried out by humans, there is an ever increasing interest in the automation of pollen monitoring, with the goal of reducing monitoring time in order to plan more efficient treatments. In this context, an original device based on computer vision is developped. In this paper, the colour segmentation techniques implemented on a hardware architecture are presented. The goal of such a system is to provide accurate measurement of pollen concentration. This information can be used as well by palynologists, clinicians or by a forecast system to predict pollen dispersion. The system is composed of two modules: pollen grain extraction and pollen grain recognition. In the first module, the pollen grains are observed in light microscopy and are extracted automatically from a pollen slide coloured with fuchsin and digitized in 3D. In the second module, the pollen grains are analyzed for recognition. To accomplish recognition, it is necessary to work on 3D images and to use deep palynological knowledge. This knowledge describes the pollen types according to their main visible characteristerics and to those which are important for recognition. Some pollen structures are identified, like the pore with annulus in Poaceae, the reticulum in Olea and similar pollen types or the cytoplasm in Cupressaceae. Preliminary results show correct recognition of some pollen types, like Urticaceae or Poaceae, and some groups of pollen types, like reticulate group.