Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, PR China.
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, United States of America.
PLoS One. 2021 May 12;16(5):e0250631. doi: 10.1371/journal.pone.0250631. eCollection 2021.
Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. EMDS-5 has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. Image denoising uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the aspect of edge detection, six edge detection operators are used to detect the edges of the images, and two evaluation indicators, peak-signal to noise ratio and mean structural similarity, are used for evaluation. Image segmentation includes single-object image segmentation and multi-object image segmentation. Six methods are used for single-object image segmentation, while k-means and U-net are used for multi-object segmentation. We extract nine features from the images in EMDS-5 and use the Support Vector Machine (SVM) classifier for testing. In terms of image classification, we select the VGG16 feature to test SVM, k-Nearest Neighbors, Random Forests. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval. We select the last layer of features of VGG16 network and ResNet50 network as feature vectors. We use mean average precision as the evaluation index for retrieval. EMDS-5 is available at the URL:https://github.com/NEUZihan/EMDS-5.git.
环境微生物数据集第五版 (EMDS-5) 是一个包含原始环境微生物 (EM) 图像和两组地面实况 (GT) 图像的微观图像数据集。GT 图像集包括单目标 GT 图像集和多目标 GT 图像集。EMDS-5 有 21 种 EM，每种 EM 包含 20 张原始 EM 图像、20 张单目标 GT 图像和 20 张多目标 GT 图像。EMDS-5 可以实现对图像预处理、图像分割、特征提取、图像分类和图像检索功能的评估。为了证明 EMDS-5 的有效性，我们针对每个功能选择了最具代表性的算法和价格指标进行测试和评估。图像预处理功能包含图像去噪和图像边缘检测两部分。图像去噪使用九种滤波器分别对 13 种噪声进行去噪。在边缘检测方面，使用六种边缘检测算子检测图像的边缘，使用两个评价指标，即峰值信噪比和平均结构相似度进行评价。图像分割包括单目标图像分割和多目标图像分割。单目标图像分割使用六种方法，多目标分割使用 k-means 和 U-net。我们从 EMDS-5 中的图像中提取了九个特征，并使用支持向量机 (SVM) 分类器进行测试。在图像分类方面，我们选择 VGG16 特征来测试 SVM、k-Nearest Neighbors 和 Random Forests。我们测试了两种检索方法：纹理特征检索和深度学习特征检索。我们选择 VGG16 网络和 ResNet50 网络的最后一层特征作为特征向量。我们使用平均精度作为检索的评价指标。EMDS-5 可在 URL：https://github.com/NEUZihan/EMDS-5.git 获得。
