A Tuning Method for Diatom Segmentation Techniques
A Tuning Method for Diatom Segmentation Techniques
复制标题
硅藻分割技术的调整方法
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. M. Menéndez
中科院分区：
文献类型：
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作者：
Oswaldo Rojas Camacho;M. Forero;J. M. Menéndez
浮游植物如硅藻或硅藻可用于监测水质。由于这组微藻的巨大多样性和其巨大的形态可塑性，手动图像分析是不切实际的，因此自动化分析过程的重要性。浮游植物细胞的高分辨率图像现在可以通过数字显微镜获得，这有助于自动化样本的分析和识别过程。因此，新的图像分析系统与用于溶液识别的手动计数方法相比具有潜在的优势。浮游植物图像分割是浮游植物图像分析的重要步骤。许多标准的技术，如阈值和边缘检测在硅藻和其他浮游植物，这是显微镜图像中的重要生物体的分割。然而，一般来说，它们需要用户预先固定几个参数，以获得最佳结果。这个过程通常是通过比较结果并寻找最佳参数来完成的。为了自动化这个过程中，我们提出了一个自动调整的方法来找到最佳参数的迭代过程中，称为参数分割调整（PST）。该技术比较连续的分割结果，选择获得最大相似性的分割结果。在本文中，调整制定为一个优化问题，在解决方案空间内使用的相似性函数。该空间包括由待调整的分割技术生成的二进制图像的集合，其中这些二进制图像被视为原始图像和分割参数的函数。PST技术进行了测试与两个最流行的技术，用于分割浮游植物图像：Canny边缘检测和二值化方法。阈值技术的结果进行了验证，通过比较它们的大津方法和Canny方法与地面真理。结果表明，PST是有效的，以找到最佳的参数。
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.
影响因子：
3
作者：
Kloster M;Kauer G;Beszteri B
通讯作者：
Beszteri B