Automatic segmentation of diatom images for classification

Abstract

A general framework for automatic segmentation of diatom images is presented. This segmentation is a critical first step in contour-based methods for automatic identification of diatoms by computerized image analysis. We review existing results, adapt popular segmentation methods to this difficult problem, and finally develop a method that substantially improves existing results. This method is based on the watershed segmentation from mathematical morphology, and belongs to the class of hybrid segmentation techniques. The novelty of the method is the use of connected operators for the computation and selection of markers, a critical ingredient in the watershed method to avoid over-segmentation. All methods considered were used to extract binary contours from a large database of diatom images, and the quality of the contours was evaluated both visually and based on identification performance.