Braiki Marwa, Benzinou Abdesslam, Nasreddine Kamal, Hymery Nolwenn
ENIB, UMR CNRS 6285 LabSTICC, 29238, Brest, France; UTM, ISTMT, LR13ES07 (LRBTM), 1006, Tunis, Tunisie.
ENIB, UMR CNRS 6285 LabSTICC, 29238, Brest, France.
Comput Methods Programs Biomed. 2020 Oct;195:105520. doi: 10.1016/j.cmpb.2020.105520. Epub 2020 May 22.
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.
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.
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.
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.
如今，与食物相关的病理情况日益增多。霉菌毒素是最严重的食品污染物之一，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来评估它们对免疫反应的影响。最近，一种使用人类树突状细胞的新研究方法得到了生物学家的认可。然而，对这些细胞的形态特征和行为的分析仍仅停留在视觉层面。此外，这种手动分析既困难又耗时。在此，我们主要致力于通过使用先进的图像处理技术来实现评估过程的自动化。
开发了一种用于显微镜下树突状细胞图像的自动分割方法，以提供快速且客观的评估。首先，结合K均值聚类和数学形态学来检测树突状细胞。其次，使用基于区域的Chan-Vese活动轮廓模型更精确地分割检测到的细胞。最后，通过基于偏心率测量的滤波提取树突状细胞。
所提出的方案在包含421张显微镜下树突状细胞图像的实际数据集上进行了测试。实验结果表明，所提出方案的结果与生物专家精心制作的真实情况高度一致。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，它给出了最高的平均准确率（99.42%）。
所提出的用于树突抑制形态分析的图像分割方法可以持续用作生物学家的评估工具，以促进对霉菌毒素严重健康影响的评估。
