A Tuning Method for Diatom Segmentation Techniques

• Published in: Applied sciences 2017-07, Vol.7 (8), p.762
• Main Authors: Rojas Camacho, Oswaldo, Forero, Manuel, Menéndez, José
• Format: Article
• Language: English
• Subjects: Automation; Desmids; diatom; Digital imaging; Edge detection; Environmental monitoring; High resolution; Identification methods; Image acquisition; Image analysis; Image detection; Image processing; Image quality; Microscopes; Microscopy; Optimization; Phytoplankton; Plankton; Pollution monitoring; Process parameters; segmentation; Similarity; Solution space; thresholding; tuning; Water quality
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app7080762

Phytoplankton such as diatoms or desmids are useful for monitoring water quality. Manual image analysis is impractical due to the huge diversity of this group of microalgae and its great morphological plasticity, hence the importance of automating the analysis procedure. High-resolution images of phytoplankton cells can now be acquired by digital microscopes, which facilitate automating the analysis and identification process of specimens. Therefore, new systems of image analysis are potentially advantageous compared to manual methods of counting for solution identification. Segmentation is an important step in the analysis of phytoplankton images. Many standard techniques like thresholding and edge detection are employed in the segmentation of diatoms and other phytoplankton, which are crucial organisms in microscopy images. However, in general, they require several parameters to be fixed beforehand by the user in order to get the best results. This process is usually done by comparing results and looking for the best parameters. To automatize this process, we propose an automatic tuning method to find the optimal parameters in an iterative procedure, called Parametric Segmentation Tuning (PST). This technique compares successive segmentation results, choosing the ones that gets the maximal similarity. In this paper, tuning is formulated as an optimization problem using a similarity function within the solution space. This space consists of the set of binary images that are generated by the segmentation technique to be tuned, where these binary images are seen as a function of the original images and the segmentation parameters. The PST technique was tested with two of the most popular techniques employed to segment phytoplankton images: the Canny edge detection and a binarisation method. The results of the thresholding technique were validated by comparing them to those of the Otsu method and the Canny method with a ground truth. They show that PST is effective to find the best parameters.