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Ma F, Wang S, Guo Y, Dai C, Meng J. Image segmentation of mouse eye in vivo with optical coherence tomography based on Bayesian classification. BIOMED ENG-BIOMED TE 2024; 69:307-315. [PMID: 38178615 DOI: 10.1515/bmt-2023-0266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/22/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVES Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images. METHODS We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification (LPBC) model. The proposed method includes training and testing phases. During the training phase, firstly, we manually select the sub-images of background and Region of Interest (ROI) from the training image, and then extract features by patches. Finally, we train the Bayesian model with the features. The segmentation threshold of each patch is computed by the learned Bayesian model. RESULTS In addition, we have collected a new dataset of mouse eyes in vivo with OCT, named MEVOCT, which can be found at URL https://17861318579.github.io/LPBC. MEVOCT consists of 20 high-resolution images. The resolution of every image is 2048 × 2048 pixels. CONCLUSIONS The experimental results demonstrate the effectiveness of the LPBC method on the new MEVOCT dataset. The ROI segmentation is of great importance for the distortion correction.
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Affiliation(s)
- Fei Ma
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Shengbo Wang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Yanfei Guo
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Cuixia Dai
- Department of College Science, Shanghai Institute of Technology, Shanghai, Shanghai, China
| | - Jing Meng
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
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Xing J, Zhou X, Zhao H, Chen H, Heidari AA. Elite levy spreading differential evolution via ABC shrink-wrap for multi-threshold segmentation of breast cancer images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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3
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Zhang W, Sun X, Zhou L, Xie X, Zhao W, Liang Z, Zhuang P. Dual-branch collaborative learning network for crop disease identification. FRONTIERS IN PLANT SCIENCE 2023; 14:1117478. [PMID: 36844059 PMCID: PMC9950499 DOI: 10.3389/fpls.2023.1117478] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Crop diseases seriously affect the quality, yield, and food security of crops. redBesides, traditional manual monitoring methods can no longer meet intelligent agriculture's efficiency and accuracy requirements. Recently, deep learning methods have been rapidly developed in computer vision. To cope with these issues, we propose a dual-branch collaborative learning network for crop disease identification, called DBCLNet. Concretely, we propose a dual-branch collaborative module using convolutional kernels of different scales to extract global and local features of images, which can effectively utilize both global and local features. Meanwhile, we embed a channel attention mechanism in each branch module to refine the global and local features. Whereafter, we cascade multiple dual-branch collaborative modules to design a feature cascade module, which further learns features at more abstract levels via the multi-layer cascade design strategy. Extensive experiments on the Plant Village dataset demonstrated the best classification performance of our DBCLNet method compared to the state-of-the-art methods for the identification of 38 categories of crop diseases. Besides, the Accuracy, Precision, Recall, and F-score of our DBCLNet for the identification of 38 categories of crop diseases are 99.89%, 99.97%, 99.67%, and 99.79%, respectively. 811.
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Affiliation(s)
- Weidong Zhang
- School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
| | - Xuewei Sun
- School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
| | - Ling Zhou
- School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
| | - Xiwang Xie
- School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China
| | - Wenyi Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China
| | - Zheng Liang
- Internet Academy, Anhui University, Hefei, Anhui, China
| | - Peixian Zhuang
- School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
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Hao S, Huang C, Heidari AA, Xu Z, Chen H, Althobaiti MM, Mansour RF, Chen X. Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Li S, Li Z, Li Q, Zhang M, Li L. Hybrid improved capuchin search algorithm for plant image thresholding. FRONTIERS IN PLANT SCIENCE 2023; 14:1122788. [PMID: 36778683 PMCID: PMC9909333 DOI: 10.3389/fpls.2023.1122788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 01/02/2023] [Indexed: 06/18/2023]
Abstract
With the development and wider application of meta-heuristic optimization algorithms, researchers increasingly apply them to threshold optimization of multi-level image segmentation. This paper explores the performance and effects of Capuchin Search Algorithm (CAPSA) in threshold optimization. To solve problems of uneven distribution in the initial population of Capuchin Search Algorithm, low levels of global search performance and premature falling into local optima, this paper proposes an improved Capuchin Search Algorithm (ICAPSA) through a multi-strategy approach. ICAPSA uses chaotic opposite-based learning strategy to initialize the positions of individual capuchins, and improve the quality of the initial population. In the iterative position updating process, Levy Flight disturbance strategy is introduced to balance the global optimization and local exploitation of the algorithm. Finally, taking Kapur as the objective function, this paper applies ICAPSA to multi-level thresholding in the plant images, and compares its segmentation effects with the original CAPSA, the Fuzzy Artificial Bee Colony algorithm (FABC), the Differential Coyote Optimization Algorithm (DCOA), the Modified Whale Optimization Algorithm (MWOA) and Improved Satin Bowerbird Optimization Algorithm (ISBO). Through comparison, it is found that ICAPSA demonstrates superior segmentation effect, both in the visual effects of image segmentation and in data comparison.
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Affiliation(s)
- Shujing Li
- School of Computer and Information Engineering, Fuyang Normal University, Fuyang, China
| | - Zhangfei Li
- School of Computer and Information Engineering, Fuyang Normal University, Fuyang, China
| | - Qinghe Li
- School of Computer and Information Engineering, Fuyang Normal University, Fuyang, China
| | - Mingyu Zhang
- School of Computer and Information Engineering, Fuyang Normal University, Fuyang, China
| | - Linguo Li
- School of Computer and Information Engineering, Fuyang Normal University, Fuyang, China
- School of Computer, Nanjing University of Posts and Telecommunications, Nanjing, China
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Li J, Liu K, Hu Y, Zhang H, Heidari AA, Chen H, Zhang W, Algarni AD, Elmannai H. Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+. Comput Biol Med 2022; 158:106501. [PMID: 36635120 DOI: 10.1016/j.compbiomed.2022.106501] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 01/11/2023]
Abstract
Computerized tomography (CT) is of great significance for the localization and diagnosis of liver cancer. Many scholars have recently applied deep learning methods to segment CT images of liver and liver tumors. Unlike natural images, medical image segmentation is usually more challenging due to its nature. Aiming at the problem of blurry boundaries and complex gradients of liver tumor images, a deep supervision network based on the combination of high-efficiency channel attention and Res-UNet++ (ECA residual UNet++) is proposed for liver CT image segmentation, enabling fully automated end-to-end segmentation of the network. In this paper, the UNet++ structure is selected as the baseline. The residual block feature encoder based on context awareness enhances the feature extraction ability and solves the problem of deep network degradation. The introduction of an efficient attention module combines the depth of the feature map with spatial information to alleviate the uneven sample distribution impact; Use DiceLoss to replace the cross-entropy loss function to optimize network parameters. The liver and liver tumor segmentation accuracy on the LITS dataset was 95.8% and 89.3%, respectively. The results show that compared with other algorithms, the method proposed in this paper achieves a good segmentation performance, which has specific reference significance for computer-assisted diagnosis and treatment to attain fine segmentation of liver and liver tumors.
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Affiliation(s)
- Jian Li
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Kongyu Liu
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Yating Hu
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Hongchen Zhang
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Ali Asghar Heidari
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
| | - Weijiang Zhang
- College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
| | - Abeer D Algarni
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
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Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition. Comput Biol Med 2022; 147:105752. [DOI: 10.1016/j.compbiomed.2022.105752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/22/2022]
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Su H, Zhao D, Yu F, Heidari AA, Zhang Y, Chen H, Li C, Pan J, Quan S. Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images. Comput Biol Med 2022; 142:105181. [PMID: 35016099 DOI: 10.1016/j.compbiomed.2021.105181] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 12/20/2021] [Accepted: 12/24/2021] [Indexed: 11/03/2022]
Abstract
The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.
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Affiliation(s)
- Hang Su
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Fanhua Yu
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Yu Zhang
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China.
| | - Chengye Li
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Jingye Pan
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China; Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Provincial, Wenzhou, Zhejiang, 325000, China; Wenzhou Key Laboratory of Critical Care and Artificial Intelligence, Wenzhou, Zhejiang, 325000, China.
| | - Shichao Quan
- Department of General Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China; Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China; Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, Zhejiang, 325000, China.
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