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.

En este artículo se describe una nueva técnica de segmentación paramétrica (TSP) de imágenes formulada como un problema de optimización de una función objetivo, la solución factible está contenida en el espacio binario u-dimensional y está compuesta por un conjunto de imágenes binarias las cuales son generadas a partir de la imagen original y del ajuste de ciertos parámetros. La función objetivo es una función de similitud y el argumento del optimo son los parámetros óptimos de la segmentación. La TSP minimiza el error de comparar la imagen segmentada en el paso n con la imagen segmentada en el paso n-1. La TSP emplea un algoritmo de segmentación basado en técnicas de morfología matemática como la transformada watershed. Esta técnica se utilizó para automatizar y optimizar los algoritmos clásicos de segmentación como: la detección de bordes de Canny, la binarización por umbralización del histograma de Otsu y la transformada watershed. La técnica se validó por el análisis ROC.

In this research work, images and segmentation algorithms in the area of microscopy were analyzed. From this analysis, the need to automatically find the contours of the cells or the region of influence was identified. Optimal models based on the regular form of the cells appear in the literature on segmentation. Unfortunately, in the general case, cells have neither regular nor specific form, hence it became necessary to develop a new segmentation technique. As a result, a new technique is presented for finding the optimal watershed (TW) transform independently of the shape of the objects in the image.