Braiki Marwa, Nasreddine Kamal, Benzinou Abdesslam, Hymery Nolwenn
ENIB, UMR CNRS 6285 LabSTICC, 29238 Brest, France.
Univ Brest, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, 29280 Plouzané, France.
J Imaging. 2023 Jan 6;9(1):13. doi: 10.3390/jimaging9010013.
Nowadays, foodborne illness is considered one of the most outgrowing diseases in the world, and studies show that its rate increases sharply each year. Foodborne illness is considered a public health problem which is caused by numerous factors, such as food intoxications, allergies, intolerances, etc. Mycotoxin is one of the food contaminants which is caused by various species of molds (or fungi), which, in turn, causes intoxications that can be chronic or acute. Thus, even low concentrations of Mycotoxin have a severely harmful impact on human health. It is, therefore, necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, researchers have approved a new method of investigation using human dendritic cells, yet the analysis of the geometric properties of these cells is still visual. Moreover, this type of analysis is subjective, time-consuming, and difficult to perform manually. In this paper, we address the automation of this evaluation using image-processing techniques. Automatic classification approaches of microscopic dendritic cell images are developed to provide a fast and objective evaluation. The first proposed classifier is based on support vector machines (SVM) and Fisher's linear discriminant analysis (FLD) method. The FLD-SVM classifier does not provide satisfactory results due to the significant confusion between the inhibited cells on one hand, and the other two cell types (mature and immature) on the other hand. Then, another strategy was suggested to enhance dendritic cell recognition results that are emitted from microscopic images. This strategy is mainly based on fuzzy logic which allows us to consider the uncertainties and inaccuracies of the given data. These proposed methods are tested on a real dataset consisting of 421 images of microscopic dendritic cells, where the fuzzy classification scheme efficiently improved the classification results by successfully classifying 96.77% of the dendritic cells. The fuzzy classification-based tools provide cell maturity and inhibition rates which help biologists evaluate severe health impacts caused by food contaminants.
如今，食源性疾病被认为是世界上增长最快的疾病之一，研究表明其发病率每年都在急剧上升。食源性疾病被视为一个公共卫生问题，它由多种因素引起，如食物中毒、过敏、不耐受等。霉菌毒素是由各种霉菌（或真菌）产生的食品污染物之一，进而导致可能是慢性或急性的中毒。因此，即使是低浓度的霉菌毒素也会对人类健康产生严重的有害影响。所以，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，研究人员批准了一种使用人类树突状细胞的新研究方法，但对这些细胞几何特性的分析仍然是可视化的。此外，这种类型的分析具有主观性、耗时且手动执行困难。在本文中，我们使用图像处理技术来解决这种评估的自动化问题。开发了微观树突状细胞图像的自动分类方法，以提供快速且客观的评估。首先提出的分类器基于支持向量机（SVM）和Fisher线性判别分析（FLD）方法。由于受抑制细胞与其他两种细胞类型（成熟和未成熟）之间存在明显混淆，FLD - SVM分类器没有提供令人满意的结果。然后，提出了另一种策略来增强从微观图像中得出的树突状细胞识别结果。该策略主要基于模糊逻辑，它使我们能够考虑给定数据的不确定性和不准确性。这些提出的方法在一个由421张微观树突状细胞图像组成的真实数据集上进行了测试，其中模糊分类方案通过成功分类96.77%的树突状细胞有效地提高了分类结果。基于模糊分类的工具提供细胞成熟度和抑制率，这有助于生物学家评估食品污染物对健康造成的严重影响。
