Environmental microorganism image retrieval using multiple colour channels fusion and particle swarm optimisation

Zou, Yanling; Li, Chen; Shirahama, Kimiaki; Jiang, Tao; Grzegorzek, Marcin

doi:10.1109/icip.2016.7532804

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…In our work, we use Environmental Microorganism Data Set 5th Version (EMDS-5), which is a newly released version of EMDS series [ 32 ], containing 21 EM classes as shown in Figure 14 . Each EM class contains 20 original microscopic images and their corresponding GT images, thus the data set includes 420 scenes.…”

Section: Experiments and Analysismentioning

confidence: 99%

A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

Jing-hua

Kulwa

et al. 2020

BioMed Research International

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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.

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Section: Experiments and Analysismentioning

confidence: 99%

A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

Jing-hua

Kulwa

et al. 2020

BioMed Research International

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“…In our work, we use the Environmental Microorganism Data Set 5th Version (EMDS-5) [9], containing 21 classes of EMs {ω 1 , ..., ω 21 } as shown in Fig. 6.…”

Section: Experiments a Experimental Setting 1) Image Datasetsmentioning

confidence: 99%

“…In (III) and (IV), we use the MBRs to crop the EMs in the original images and rotate the images to make EMs' main axis horizontal. In (V), because the colors of images have little effect on their class label [9] [10], we perform color space augmentations on these images. • In step-2, we input the images from step-1 (V) into the GANs to generate images, which augment EM images four times.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Rahaman

et al. 2020

IEEE Access

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The main obstacle to image augmentation with Generative Adversarial Networks (GANs) is the need for a large amount of training data, but this is difficult for small datasets like Environmental Microorganisms (EMs). EM image analysis plays a vital role in environmental monitoring and protection, but it is often encountered with small datasets due to the difficulty of EM image collection. To this end, we propose an Enhanced Framework of GANs (EF-GANs) that combines geometric transformation methods and GANs for EM image augmentation. First of all, the color of an EM image has an insignificant impact on its class label, based on this fact, we perform color space augmentation to the original EM images. Secondly, we train EF-GANs with augmented EM images to generate utterly new EM images. Finally, we rotate the generated samples in various directions to obtain a more natural performance. In this study, we use VGG16 and ResNet50 networks to evaluate the proposed EF-GANs on 21 different types of EMs (420 EM images). It is observed that the average precision (AP) of VGG16 increases between 4.5% and 84.1% in 20 EM classes and one class remains unchanged. The AP of Resnet50 rises between 8.7% and 38.7% in 12 EM classes and reaches 100% in two EM classes. Furthermore, to reflect the generalization performance of EF-GANs, we employ an utterly new EM image dataset (630 EM images) to test the previous VGG16 networks. We select the VGG16 networks with original and optimal settings for all the EM classes, and for testing, optimal settings for a single EM class is considered. In the 20 of 21 one-vs-rest EM image classification tasks, the AP of VGG16 increases between 1.66% and 88.1%. The results demonstrate that the proposed EF-GANs can achieve outstanding performance in augmenting single EM images with high quality and resolution, thus, to improve the APs of EM image classification.

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“…However, the local shape features begin to show a strong stamina in recent years, e.g. the work in Li et al (2015c), Zou et al (2016b). Furthermore, the global and local shape features usually have a complementarity in different scale spaces, so we can combine their describing abilities to enhance the final classification performance.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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Environmental microorganism image retrieval using multiple colour channels fusion and particle swarm optimisation

Cited by 13 publications

References 18 publications

A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

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