The image processing of muscular fibers has seen significant improvements over the last decades, from light microscopy acquisition to virtual microscopy and from manual segmentation and fiber typing to automatic. The present study aims to discuss the main tools employed in different histo-enzymological image processing phases applied to muscle fibers, which are respectively: the acquisition, segmentation and fiber typing as well as their efficiency in the determination of morphometric parameters.

First, the acquisition: to count the cell numbers within the muscle fiber sections, optical microscopic images with different magnifications (x100, x200, x250, and x400) were compared with virtual slides digitized by Slide Scanner. Second, the segmentation: Three softwares (Fiji “Digitizing pen, Mouse”, Image Pro Plus 10 [semi-automatic], and Cytoinformatics LLC [automatic]) were compared for image segmentation quality and the determining of fiber numbers. Third, manual fiber typing: employing Fiji of segmented images using the 3 previously mentioned softwares were performed to calculate the accuracy of the morphometric parameter (Cross sectional Area [CSA], perimeter and Minimal Feret's Diameter [MFD]). The results of the acquisition showed that the scanner slides have a better resolution and detected a higher number of cells than when optical microscopes with different magnifications were used; with the latter, the processing can be performed only with an acceptable resolution, where the number of cells is lesser, which unfortunately requires several repetitions and exhausting work. Our findings regarding segmentation indicate that the Cytoinformatics LLC showed the best processing time and the highest quality followed by IP and Fiji. Finally and for the ultimate step, the morphometric parameter calculation: showed that the best accuracy was attained by using Fiji followed by Cytoinformatics and finally by IP. The findings of this study suggest that Fiji (semi-automatic) showed the best quality/price ratio (open access software) for segmentation and fiber typing, but it proved to be time consuming when compared to Image Pro Plus 10 (semi-automatic) and Cytoinformatics LLC (automatic).

在过去的几十年里，肌肉纤维的图像处理有了显着的改进，从光学显微镜采集到虚拟显微镜，从手动分割和纤维打字到自动。本研究旨在讨论应用于肌肉纤维的不同组织酶学图像处理阶段所采用的主要工具，分别是：采集、分割和纤维分型以及它们在确定形态参数方面的效率。

首先，采集：为了计算肌纤维切片内的细胞数量，将不同放大倍数（x100、x200、x250 和 x400）的光学显微图像与 Slide Scanner 数字化的虚拟载玻片进行比较。二、分割：比较了三种软件（Fiji“Digitizing pen, Mouse”、Image Pro Plus 10 [semi-automatic] 和 Cytoinformatics LLC [automatic]）的图像分割质量和纤维数量的确定。第三，手动光纤打字：使用前面提到的 3 种软件对分割图像进行斐济，以计算形态测量参数（横截面积 [ CSA ]、周长和最小费雷特直径 [ MFD ]的准确性）]）。采集结果表明，与使用不同放大倍数的光学显微镜相比，扫描仪载玻片具有更好的分辨率并检测到更多的细胞；对于后者，处理只能以可接受的分辨率执行，其中单元格的数量较少，不幸的是，这需要多次重复和令人筋疲力尽的工作。我们关于分割的发现表明 Cytoinformatics LLC 显示出最佳的处理时间和最高的质量，其次是 IP 和斐济。最后，也是最后一步，形态测量参数计算：表明使用斐济，然后是细胞信息学，最后是 IP，获得了最佳准确度。