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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). [ABSTRACT FROM AUTHOR]

[Display omitted] • A total of 13 different edible and non-edible oils are tested under image processing. • 1300 images are captured under a fluorescent lamp with CANON-EOS-200D digital camera. • KNN and SVM models are used for validation. • High Accuracy RGB image pixel extraction process with Fiji. • Significant properties of oils are predicted accurately. Renewable oils from different feedstocks are considered a potential replacement for the current petroleum products, and they are widely used in various applications like alternative fuels to heat engines and bio-coolants for metal removal operations, and their analysis of significant physicochemical properties is significant before their application. This study attempts to analyze the significant oil properties of different edible and non-edible oils using an image processing technique. For this purpose, 11 different oils are considered for experimentation, and their surface images of all the oils are captured and utilized to extract significant pixel information of Red, Green and Blue (RGB). The RGB pixel information is correlated with experimental properties (viscosity, density, flash point, fire point, cloud and pour points) and the developed model is validated with KNN, SVM and Fiji techniques and observed a high prediction accuracy (99%, 90.9% and 99%). Finally, two different oils which are not used in the experiment are chosen to test the prediction accuracy of the developed model. The results show that the RGB pixel predicts the significant oil properties, and they are validated experimentally and observed that the developed model predicts the oil properties with 91.7% accuracy. [ABSTRACT FROM AUTHOR]