  Image Analysis offers a new tool for classifying vascular plant species based
on the morphological and colorimetric features of the seeds, and has made
significant contributions in systematic studies. However, in order to extract
the morphological and colorimetric features, it is necessary to segment the
image containing the samples to be analysed. This stage represents one of the
most challenging steps in image processing, as it is difficult to separate
uniform and homogeneous objects from the background. In this paper, we present
a new, open source plugin for the automatic segmentation of an image of a seed
sample. This plugin was written in Java to allow it to work with ImageJ open
source software. The new plugin was tested on a total of 3,386 seed samples
from 120 species belonging to the Fabaceae family. Digital images were acquired
using a flatbed scanner. In order to test the efficacy of this approach in
terms of identifying the edges of objects and separating them from the
background, each sample was scanned using four different hues of blue for the
background, and a total of 480 digital images were elaborated. The performance
of the new plugin was compared with a method based on double image acquisition
(with a black and white background) using the same seed samples, in which
images were manually segmented using the Core ImageJ plugin. The results showed
that the new plugin was able to segment all of the digital images without
generating any object detection errors. In addition, the new plugin was able to
segment images within an average of 0.02 s, while the average time for
execution with the manual method was 63 s. This new open source plugin is
proven to be able to work on a single image, and to be highly efficient in
terms of time and segmentation when working with large numbers of images and a
wide diversity of shapes.
