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首页> 外文期刊>Journal of dairy research >Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques
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Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques

机译：使用模糊聚类和图像处理技术的水牛牛奶体细胞计数

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摘要

This research communication presents an automatic method for the counting of somatic cells in buffalo milk, which includes the application of a fuzzy clustering method and image pro-cessing techniques (somatic cell count with fuzzy clustering and image processingl, SCCFCI). Somatic cell count (SCC) in milk is the main biomarker for assessing milk quality and it is traditionally performed by exhaustive methods consisting of the visual observation of cells in milk smears through a microscope, which generates uncertainties associated with human interpretation. Unlike other similar works, the proposed method applies the Fuzzy C-Means (FCM) method as a preprocessing step in order to separate the images (objects) of the cells into clusters according to the color intensity. This contributes signficantly to the performance of the subsequent processing steps (thresholding, segmentation and recogni-tion/identification). Two methods of thresholding were evaluated and the Watershed Transform was used for the identification and separation of nearby cells. A detailed statistical analysis of the results showed that the SCCFCI method is able to provide results which are consistent with those obtained by conventional counting. This method therefore represents a viable alternative for quality control in buffalo milk production.
机译：该研究通信呈现了一种自动计算水牛牛奶中体细胞的自动方法，包括应用模糊聚类方法和图像的应用（具有模糊聚类和图像处理，SCCFCI的体细胞计数）。牛奶中的体细胞计数（SCC）是用于评估牛奶质量的主要生物标志物，传统上是通过通过显微镜通过显微镜的牛奶涂片中细胞的视觉观察组成的详尽方法进行，这产生了与人类解释相关的不确定性。与其他类似的作品不同，所提出的方法将模糊C-MEARS（FCM）方法应用于预处理步骤，以便根据颜色强度将单元的图像（对象）分离成簇。这显着地贡献了后续处理步骤的性能（阈值，分割和识别/识别）。评估两种阈值化方法，流域变换用于附近细胞的鉴定和分离。结果的详细统计分析表明，SCCFCI方法能够提供与通过常规计数获得的结果一致的结果。因此，该方法代表了水牛牛奶生产中质量控制的可行替代方案。

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