Segmentation of diatoms using edge detection and deep learning

GÜNDÜZ H., Solak C. N., Günal S.

Turkish Journal of Electrical Engineering and Computer Sciences, vol.30, no.6, pp.2268-2285, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 6
  • Publication Date: 2022
  • Doi Number: 10.55730/1300-0632.3938
  • Journal Name: Turkish Journal of Electrical Engineering and Computer Sciences
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.2268-2285
  • Keywords: deep learning, Diatom, segmentation
  • Anadolu University Affiliated: Yes


© 2022 Turkiye Klinikleri. All rights reserved.Diatoms are photosynthesizing algae found in almost every aquatic environment. Detecting the number and diversity of diatoms is very important to analyze water quality appropriately. Accurate segmentation of diatoms is therefore crucial for this detection process. In this study, a new and effective model for the automatic segmentation of diatoms based on image processing and deep learning algorithms is proposed. In the proposed model, edge segments of a given image containing diatoms and nondiatom particles are first obtained. These edge segments are then combined, resulting in closed contours representing diatom candidates. In the final step, the diatom candidates are classified as either diatom or nondiatom using a transfer learning approach with different pretrained deep neural network models including AlexNet, ResNet18, VGG16, and their ensemble. Hence, the boundary of a diatom candidate, which is classified as a diatom, is considered to be the final output of the proposed segmentation model. Besides, a new and annotated diatom image dataset is introduced. The dataset is divided into two parts. The first part is used to train and evaluate the classification stage of the proposed model, while the second part is used for evaluating the overall performance of the proposed segmentation model. This dataset is also made publicly available at https: // for the research community. Through extensive experimental work, the performance of the proposed segmentation model is measured in terms of precision, recall, F-measure, and intersection over union values. Our work is also compared with the previous works based on various aspects such as segmentation method, dataset size, diatom varieties, dataset availability, success metric, and segmentation performance. Considering all these aspects, it is verified that the proposed model surpasses the previous works, and stands out as a well-performing approach to the automatic segmentation of diatoms.