{"title":"Comparison of threshold algorithms for automatic processing of fat crystal microscopic images based on ImageJ","authors":"Miao Xiong, Ang Qi, Lu Zhang","doi":"10.1002/aocs.12846","DOIUrl":null,"url":null,"abstract":"<p>Microscopic image analysis is a crucial tool in fat crystallization research, enabling the analysis of crystal size, network structure, fractal dimension and other parameters through binarization. It is essential to seek an appropriate thresholding algorithm to binarize fat crystal images, which plays a vital role in image segmentation. In this article, the effectiveness of 17 thresholding algorithms such as Default, Mean, IsoData, Otsu, Li and Triangle were analyzed in processing fat crystal images with different shapes, background colors and image intensities. This was expected to discover a stable and objective thresholding algorithm for the binarization of fat crystal images. The performance evaluation was conducted according to the peak signal noise ratio (PSNR), structural similarity index (SSIM) and region non-uniformity (RNU) parameter. Moreover, the comparative analysis of crystal size error, crystal area fraction and intraclass correlation coefficients (ICC) for fractal dimension values would provide a foundation for the selection of thresholding techniques for fat crystal network images. The results indicated that the Default algorithm exhibited remarkable robustness and applicability with high-quality and stable outputs in fat crystal image processing.</p>","PeriodicalId":17182,"journal":{"name":"Journal of the American Oil Chemists Society","volume":"101 12","pages":"1455-1466"},"PeriodicalIF":1.9000,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Oil Chemists Society","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aocs.12846","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Microscopic image analysis is a crucial tool in fat crystallization research, enabling the analysis of crystal size, network structure, fractal dimension and other parameters through binarization. It is essential to seek an appropriate thresholding algorithm to binarize fat crystal images, which plays a vital role in image segmentation. In this article, the effectiveness of 17 thresholding algorithms such as Default, Mean, IsoData, Otsu, Li and Triangle were analyzed in processing fat crystal images with different shapes, background colors and image intensities. This was expected to discover a stable and objective thresholding algorithm for the binarization of fat crystal images. The performance evaluation was conducted according to the peak signal noise ratio (PSNR), structural similarity index (SSIM) and region non-uniformity (RNU) parameter. Moreover, the comparative analysis of crystal size error, crystal area fraction and intraclass correlation coefficients (ICC) for fractal dimension values would provide a foundation for the selection of thresholding techniques for fat crystal network images. The results indicated that the Default algorithm exhibited remarkable robustness and applicability with high-quality and stable outputs in fat crystal image processing.
期刊介绍：
The Journal of the American Oil Chemists’ Society (JAOCS) is an international peer-reviewed journal that publishes significant original scientific research and technological advances on fats, oils, oilseed proteins, and related materials through original research articles, invited reviews, short communications, and letters to the editor. We seek to publish reports that will significantly advance scientific understanding through hypothesis driven research, innovations, and important new information pertaining to analysis, properties, processing, products, and applications of these food and industrial resources. Breakthroughs in food science and technology, biotechnology (including genomics, biomechanisms, biocatalysis and bioprocessing), and industrial products and applications are particularly appropriate.
JAOCS also considers reports on the lipid composition of new, unique, and traditional sources of lipids that definitively address a research hypothesis and advances scientific understanding. However, the genus and species of the source must be verified by appropriate means of classification. In addition, the GPS location of the harvested materials and seed or vegetative samples should be deposited in an accredited germplasm repository. Compositional data suitable for Original Research Articles must embody replicated estimate of tissue constituents, such as oil, protein, carbohydrate, fatty acid, phospholipid, tocopherol, sterol, and carotenoid compositions. Other components unique to the specific plant or animal source may be reported. Furthermore, lipid composition papers should incorporate elements of yeartoyear, environmental, and/ or cultivar variations through use of appropriate statistical analyses.
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