Automatic Pollen Grain and Exine Segmentation from Microscope Images
This work addresses the need for automated analysis in palynology, offering a domain-specific tool for pollen classification.
The authors tackled the problem of automatically segmenting pollen grains and their exine from microscope images using a coarse-to-fine approach, achieving a method that handles various pollen types and exine appearances as part of an automatic classification framework.
In this article, we propose an automatic method for the segmentation of pollen grains from microscope images, followed by the automatic segmentation of their exine. The objective of exine segmentation is to separate the pollen grain in two regions of interest: exine and inner part. A coarse-to-fine approach ensures a smooth and accurate segmentation of both structures. As a rough stage, grain segmentation is performed by a procedure involving clustering and morphological operations, while the exine is approximated by an iterative procedure consisting in consecutive cropping steps of the pollen grain. A snake-based segmentation is performed to refine the segmentation of both structures. Results have shown that our segmentation method is able to deal with different pollen types, as well as with different types of exine and inner part appearance. The proposed segmentation method aims to be generic and has been designed as one of the core steps of an automatic pollen classification framework.