Abstract
To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.
Highlights
Environmental pollution is an extremely serious problem in many countries
To observe the advantages of combining patch-level segmentation with pixel-level segmentation better, we provide some examples and their corresponding evaluation indexes in Figures 21 and 25, respectively
We propose a multilevel segmentation method for the Environmental Microorganisms (EMs) segmentation task, which includes pixel-level segmentation and patch-level segmentation
Summary
Environmental pollution is an extremely serious problem in many countries. Many methods to deal with environmental pollution are constantly being put forward. The methods of eliminating environmental pollution can be divided into three major categories: chemical, physical, and biological. The biological method is more harmless and well efficient [1]. Environmental Microorganisms (EMs) are microscopic organisms living in the environment, which are natural decomposers and indicators [2]. Actinophrys can digest the organic waste in sludge and increase the quality of freshwater. The research on EMs plays a significant role in the management of pollution [3]. The identification of EMs is the basic step for related researches
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