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，可以通过二值化分析晶体尺寸、网络结构、分形维数和其他参数。寻找合适的阈值算法对脂肪晶体图像进行二值化至关重要，这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、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效果。这有望发现一种稳定且客观的脂肪晶体图像二值化阈值算法。根据峰值信噪比（PSNR）、结构相似性指数（SSIM）和区域不均匀性（RNU）参数进行性能评估。此外，晶体尺寸误差、晶体面积分数和分形维数值的类内相关系数（ICC）的比较分析将为脂肪晶体网络图像阈值技术的选择提供基础。结果表明，Default算法在脂肪晶体图像处理中表现出显着的鲁棒性和适用性，输出质量高且稳定。