Segmentation of weakly visible environmental microorganism images using pair-wise deep learning features. (January 2023)

• Record Type: Journal Article
• Authors: Kulwa, Frank; Li, Chen; Grzegorzek, Marcin; Rahaman, Md Mamunur; Shirahama, Kimiaki; Kosov, Sergey
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 2
• Publication Date: 2023-01
• Subjects: Microscopic images -- Transparent microorganism -- Image segmentation -- Pair-wise features -- Convolutional neural network -- Environmental microorganism images; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104168
• Languages: English
• ISSNs: 1746-8094

Abstract

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. Highlights: The proposed method is based on Shi and Tomasi corners and deep learning features. Features from patches centered at descriptors are paired by Delaunay triangulation. The pairwise features are concatenate to the SegNet to improve the performance.