Comparison of threshold algorithms for automatic processing of fat crystal microscopic images based on ImageJ

IF 1.9 4区农林科学 Q3 CHEMISTRY, APPLIED

Journal of the American Oil Chemists Society Pub Date : 2024-04-23 DOI:10.1002/aocs.12846

Miao Xiong, Ang Qi, Lu Zhang

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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.

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基于 ImageJ 的脂肪晶体显微图像自动处理阈值算法比较

显微图像分析是脂肪结晶研究的重要工具，通过二值化可以分析晶体尺寸、网络结构、分形维度和其他参数。寻找合适的阈值算法对脂肪晶体图像进行二值化处理至关重要，它在图像分割中起着至关重要的作用。本文分析了 Default、Mean、IsoData、Otsu、Li 和 Triangle 等 17 种阈值算法在处理不同形状、背景颜色和图像强度的脂肪晶体图像时的有效性。这有望为脂肪晶体图像的二值化找到一种稳定、客观的阈值算法。根据峰值信号噪声比（PSNR）、结构相似性指数（SSIM）和区域不均匀性（RNU）参数进行了性能评估。此外，对分形维度值的晶体尺寸误差、晶体面积分数和类内相关系数（ICC）进行了比较分析，为选择脂肪晶体网络图像的阈值处理技术奠定了基础。结果表明，Default 算法在脂肪晶体图像处理中表现出了显著的鲁棒性和适用性，输出质量高且稳定。

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来源期刊

Journal of the American Oil Chemists Society 工程技术-食品科技

CiteScore

4.10

自引率

5.00%

发文量

审稿时长

2.4 months

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.