Background and objective

Nowadays, the number of pathologies related to food are multiplied. Mycotoxins are one of the most severe food contaminants that cause serious effects on the human health. Therefore, it is necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, a new investigational method using human dendritic cells was endorsed by biologists. Nevertheless, analysis of the morphological features and the behavior of these cells remains merely visual. In addition, this manual analysis is difficult and time-consuming. Here, we focus mainly on automating the evaluation process by using advanced image processing technology.

Methods

An automatic segmentation approach of microscopic dendritic cell images is developed to provide a fast and objective evaluation. First, a combination of K-means clustering and mathematical morphology is used to detect dendritic cells. Second, a region-based Chan-Vese active contour model is used to segment the detected cells more precisely. Finally, dendritic cells are extracted by a filtering based on eccentricity measure.

Results

The proposed scheme is tested on an actual dataset containing 421 microscopic dendritic cell images. The experimental results show high conformity between the results of the proposed scheme and ground-truth elaborated by biological expert. Moreover, a comparative study with other state-of-art segmentation schemes demonstrates the efficiency of the proposed method. It gives the highest average accuracy rate (99.42 %) compared to recent studied approaches.

Conclusions

The proposed image segmentation method for morphological analysis of dendrite inhibition can consistently be used as an assessment tool for biologists to facilitate the evaluation of serious health impacts of mycotoxins.

中文翻译：

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使用K-Means聚类和Chan-Vese主动轮廓模型对人类树突状细胞进行自动分割。

背景和目标

如今，与食物有关的病理疾病数量成倍增加。霉菌毒素是最严重的食物污染物之一，会对人体健康造成严重影响。因此，有必要开发一种评估工具以评估其对免疫反应的影响。最近，生物学家认可了一种使用人树突状细胞的新研究方法。尽管如此，对这些细胞的形态特征和行为的分析仍然仅仅是视觉上的。另外，这种手动分析既困难又费时。在这里，我们主要致力于通过使用先进的图像处理技术来自动化评估过程。

方法

开发了显微树突状细胞图像的自动分割方法，以提供快速而客观的评估。首先，结合使用K均值聚类和数学形态学来检测树突状细胞。其次，基于区域的Chan-Vese活动轮廓模型用于更精确地分割检测到的细胞。最后，通过基于离心率测量的过滤来提取树突状细胞。

结果

在包含421个显微树突状细胞图像的实际数据集上测试了该方案。实验结果表明，该方案的结果与生物学专家精心设计的真实性高度吻合。此外，与其他最新分割方案的比较研究证明了该方法的有效性。与最近研究的方法相比，它具有最高的平均准确率（99.42％）。

结论

所提出的用于树突抑制作用形态分析的图像分割方法可以始终用作生物学家的评估工具，以帮助评估真菌毒素对健康的严重影响。