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Satrya GB, Ramatryana INA, Shin SY. Compressive Sensing of Medical Images Based on HSV Color Space. SENSORS (BASEL, SWITZERLAND) 2023; 23:2616. [PMID: 36904821 PMCID: PMC10006955 DOI: 10.3390/s23052616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 02/06/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV-SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV-SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of 256×256 pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV-SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.
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Affiliation(s)
| | - I Nyoman Apraz Ramatryana
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
| | - Soo Young Shin
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
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Chen X, Chen Y, Ma W, Fan X, Li Y. Toward sleep apnea detection with lightweight multi-scaled fusion network. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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3
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Muruganantham P, Balakrishnan SM. Attention Aware Deep Learning Model for Wireless Capsule Endoscopy Lesion Classification and Localization. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00686-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Wang W, Yang X, Li X, Tang J. Convolutional‐capsule network for gastrointestinal endoscopy image classification. INT J INTELL SYST 2022. [DOI: 10.1002/int.22815] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Wei Wang
- School of Computer Science and Engineering, Nanjing University of Science and Technology Nanjing Jiangsu China
| | - Xin Yang
- School of Electronic Information and Communications, Huazhong University of Science and Technology Wuhan Hubei China
| | - Xin Li
- Department of Radiology Union Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan Hubei China
- Hubei Province Key Laboratory of Molecular Imaging Wuhan Hubei China
| | - Jinhui Tang
- School of Computer Science and Engineering, Nanjing University of Science and Technology Nanjing Jiangsu China
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AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images. REMOTE SENSING 2021. [DOI: 10.3390/rs13245109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net.
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Zhu M, Chen Z, Yuan Y. DSI-Net: Deep Synergistic Interaction Network for Joint Classification and Segmentation With Endoscope Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3315-3325. [PMID: 34033538 DOI: 10.1109/tmi.2021.3083586] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Automatic classification and segmentation of wireless capsule endoscope (WCE) images are two clinically significant and relevant tasks in a computer-aided diagnosis system for gastrointestinal diseases. Most of existing approaches, however, considered these two tasks individually and ignored their complementary information, leading to limited performance. To overcome this bottleneck, we propose a deep synergistic interaction network (DSI-Net) for joint classification and segmentation with WCE images, which mainly consists of the classification branch (C-Branch), the coarse segmentation (CS-Branch) and the fine segmentation branches (FS-Branch). In order to facilitate the classification task with the segmentation knowledge, a lesion location mining (LLM) module is devised in C-Branch to accurately highlight lesion regions through mining neglected lesion areas and erasing misclassified background areas. To assist the segmentation task with the classification prior, we propose a category-guided feature generation (CFG) module in FS-Branch to improve pixel representation by leveraging the category prototypes of C-Branch to obtain the category-aware features. In such way, these modules enable the deep synergistic interaction between these two tasks. In addition, we introduce a task interaction loss to enhance the mutual supervision between the classification and segmentation tasks and guarantee the consistency of their predictions. Relying on the proposed deep synergistic interaction mechanism, DSI-Net achieves superior classification and segmentation performance on public dataset in comparison with state-of-the-art methods. The source code is available at https://github.com/CityU-AIM-Group/DSI-Net.
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Jain S, Seal A, Ojha A, Yazidi A, Bures J, Tacheci I, Krejcar O. A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images. Comput Biol Med 2021; 137:104789. [PMID: 34455302 DOI: 10.1016/j.compbiomed.2021.104789] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 08/18/2021] [Accepted: 08/18/2021] [Indexed: 12/22/2022]
Abstract
Wireless capsule endoscopy (WCE) is one of the most efficient methods for the examination of gastrointestinal tracts. Computer-aided intelligent diagnostic tools alleviate the challenges faced during manual inspection of long WCE videos. Several approaches have been proposed in the literature for the automatic detection and localization of anomalies in WCE images. Some of them focus on specific anomalies such as bleeding, polyp, lesion, etc. However, relatively fewer generic methods have been proposed to detect all those common anomalies simultaneously. In this paper, a deep convolutional neural network (CNN) based model 'WCENet' is proposed for anomaly detection and localization in WCE images. The model works in two phases. In the first phase, a simple and efficient attention-based CNN classifies an image into one of the four categories: polyp, vascular, inflammatory, or normal. If the image is classified in one of the abnormal categories, it is processed in the second phase for the anomaly localization. Fusion of Grad-CAM++ and a custom SegNet is used for anomalous region segmentation in the abnormal image. WCENet classifier attains accuracy and area under receiver operating characteristic of 98% and 99%. The WCENet segmentation model obtains a frequency weighted intersection over union of 81%, and an average dice score of 56% on the KID dataset. WCENet outperforms nine different state-of-the-art conventional machine learning and deep learning models on the KID dataset. The proposed model demonstrates potential for clinical applications.
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Affiliation(s)
- Samir Jain
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India
| | - Ayan Seal
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India.
| | - Aparajita Ojha
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India
| | - Anis Yazidi
- Department of Computer Science, OsloMet - Oslo Metropolitan University, Oslo, Norway; Department of Plastic and Reconstructive Surgery, Oslo University Hospital, Oslo, Norway; Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jan Bures
- Second Department of Internal Medicine-Gastroenterology, Charles University, Faculty of Medicine in Hradec Kralove and University Hospital Hradec Kralove, Sokolska 581, Hradec Kralove, 50005, Czech Republic
| | - Ilja Tacheci
- Second Department of Internal Medicine-Gastroenterology, Charles University, Faculty of Medicine in Hradec Kralove and University Hospital Hradec Kralove, Sokolska 581, Hradec Kralove, 50005, Czech Republic
| | - Ondrej Krejcar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradecka 1249, Hradec Kralove, 50003, Czech Republic; Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia
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