Automatic Human Dendritic Cells Segmentation Using K-Means Clustering and Chan-Vese Active Contour Model. (October 2020)

• Record Type: Journal Article
• Authors: Braiki, Marwa; Benzinou, Abdesslam; Nasreddine, Kamal; Hymery, Nolwenn
• Is Part Of: Computer methods and programs in biomedicine. Volume 195(2020)
• Publication Date: 2020-10
• Subjects: Segmentation -- Dendritic Cells -- Morphology -- K-means -- Active Contour; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105520
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We aim to develop an assessment tool for evaluating mycotoxins impact on the immune response. We address the problem of automatic segmentation of microscopic images of human dendritic cells. We propose a segmentation method based on the combination of K-means clustering and Chan-Vese active contour model. Performance evaluations with a comparative study are reported through an actual dataset containing 421 microscopic dendritic cell images. Abstract: 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.