Segmentation of weakly visible environmental microorganism images using pair-wise deep learning features

Frank Kulwa, Chen Li*, Marcin Grzegorzek, Md Mamunur Rahaman, Kimiaki Shirahama, Sergey Kosov

*Corresponding author for this work
2 Citations (Scopus)

Abstract

The use of Environmental Microorganisms (EMs) offers a highly efficient, low cost and harmless remedy to environmental pollution, by monitoring and decomposing of pollutants. This relies on how the EMs are correctly segmented and identified. With the aim of enhancing the segmentation of weakly visible EM images which are transparent, noisy and have low contrast, a Pairwise Deep Learning Feature Network (PDLF-Net) is proposed in this study. The use of PDLFs enables the network to focus more on the foreground (EMs) by concatenating the pairwise deep learning features of each image to different blocks of the base model SegNet. Leveraging the Shi and Tomas descriptors, we extract each image's deep features on the patches, which are centred at each descriptor using the VGG-16 model. Then, to learn the intermediate characteristics between the descriptors, pairing of the features is performed based on the Delaunay triangulation theorem to form pairwise deep learning features. In this experiment, the PDLF-Net achieves outstanding segmentation results of 89.24%, 63.20%, 77.27%, 35.15%, 89.72%, 91.44% and 89.30% on the accuracy, IoU, Dice, VOE, sensitivity, precision and specificity, respectively.

Original languageEnglish
Article number104168
JournalBiomedical Signal Processing and Control
Volume79
Pages (from-to)104168
Number of pages1
ISSN1746-8094
DOIs
Publication statusPublished - 01.2023

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

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