Fig 1.
An example of EM images.
Fig 2.
An example of 21 EM classes in EMDS-5.
Single-object GT images (SGI), Multi-object GT images (MGI).
Table 1.
Basic information of 21 EM classes in EMDS-5.
Number of original images (NoOI), Number of single-object GT images (NoSGI), Number of multi-object GT images (NoMGI), Visible characteristics (VC).
Fig 3.
An example of different noisy EM images using EMDS-5 images.
Table 2.
A comparison of similarities between denoised images and original image using EMDS-5.
(In [%].).
Table 3.
A comparison of variances between denoised images and original image using EMDS-5.
(In [%].).
Table 4.
A comparison of PSNR between denoised images and original image using EMDS-5.
Table 5.
A comparison of SSIM between denoised images and original image using EMDS-5.
(In [%].).
Fig 4.
An example of seven edge detection results using EMDS-5 images.
Table 6.
A comparison of edge detection methods using EMDS-5.
Evaluation index (EI), Operator type (OT).
Fig 5.
An example of different single-object segmentation results using EMDS-5 images.
Table 7.
The image segmentation evaluation metrics used in this paper and their definitions.
TP (True Positive), FN (False Negative), FP (False Positive).
Table 8.
A comparison of single-object segmentation methods using EMDS-5.
Image segmentation methods (ISM), Evaluation index (EI), Watershed algorithm (WA), Otsu thresholding (OT), Region growing (RG). (In [%].).
Fig 6.
The structure of U-Net.
Fig 7.
An example of different multi-object segmentation results using EMDS-5.
Table 9.
A comparison of multi-object segmentation methods using EMDS-5.
Image segmentation methods (ISM), Evaluation index (EI). (In [%].).
Fig 8.
An example of localized EMs by GT images.
Table 10.
Classification accuracy of single-object features by RBFSVM using EMDS-5. Feature type (FT), Accuracy (Acc), Geometric features (Geo), Hu moments (Hu).
(In [%].).
Table 11.
Classification accuracy of multi-object features by RBFSVM using EMDS-5. F Feature type (FT), Accuracy (Acc), Geometric features (Geo), Hu moments (Hu).
(In [%].).
Table 12.
The parameters of four SVM classifiers for EMDS-5 image classification (supported by LIBSVM).
Table 13.
A comparison of EM image classification results using EMDS-5.
Accuracy (Acc), nTree (nT), VGG16 (Train: Validation: Test = 1: 1: 2) is VGG16: 1: 1: 2, VGG16 (Train: Validation: Test = 1: 2: 1) is VGG16: 1: 2: 1, Inception-V3 (Train: Validation: Test = 1: 1: 2) is I-V3: 1: 1: 2, Inception-V3 (Train: Validation: Test = 1: 2: 1) is I-V3: 1: 2: 1. (In [%].).
Fig 9.
An example of image retrieval results with GLCM using EMDS-5.
Fig 10.
A comparison of image retrieval results with four texture features using EMDS-5.
Fig 11.
An example of image retrieval results based on VGG16 feature using EMDS-5.
Fig 12.
A comparison of image retrieval results with two deep learning features using EMDS-5.