An effective and friendly tool for seed image analysis

Image analysis is an essential field for several topics of life sciences, such as biology or botany. In particular, seeds analysis (e.g., fossil research) can provide significant information about their evolution, the history of agriculture, the domestication of plants, and the knowledge of diets in ancient times. This work aims to present a software that performs an image analysis by feature extraction and classification starting from images containing seeds through a brand new and unique framework. In detail, we propose two \emph{ImageJ} plugins, one capable of extracting morphological, textural, and colour characteristics from images of seeds, and another one to classify the seeds into categories by using the extracted features. The experimental results demonstrated the correctness and validity both of the extracted features and the classification predictions. The proposed tool is easily extendable to other fields of image analysis.

A new automatic approach to seed image analysis: From acquisition to segmentation

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.