Image Segmentation and Object Extraction for Automatic Diatoms Classification

The diatoms are unicellular algae of great interest in paleontology, aquatic ecology, and forensic medicine, among others. Currently, there are more than 100 000 known species distributed in aquatic ecosystems. For that reason, there is a big interest in the automatic classification of diatom images, however, the preliminary process applied to isolate the diatom from the background is a complex task. In this paper, we propose a segmentation method and an object-extraction procedure to extract the diatom from the background. First, we binarize the image by searching the optimal threshold in the histogram based on its cumulative distribution function. Then we eliminate, under some spatial criteria, all regions other than those that could be part of the diatom. Afterwards, we construct the convex hull of all remaining components. Finally, from this first polygonal approximation, we construct the diatom contour by successive refinements of the convex hull shape.