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Yuan R, Luo M, Sun Z, Shi S, Xiao P, Xie Q. RayPlus: a Web-Based Platform for Medical Image Processing. J Digit Imaging 2018; 30:197-203. [PMID: 27904975 DOI: 10.1007/s10278-016-9920-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities.
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Affiliation(s)
- Rong Yuan
- College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, Hubei, 430074, China
| | - Ming Luo
- College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, Hubei, 430074, China
| | - Zhi Sun
- College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, Hubei, 430074, China
| | - Shuyue Shi
- College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, Hubei, 430074, China
| | - Peng Xiao
- College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, Hubei, 430074, China
- Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
| | - Qingguo Xie
- College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Rd, Wuhan, Hubei, 430074, China.
- Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China.
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Maity M, Dhane D, Mungle T, Maiti AK, Chakraborty C. Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach. J Med Syst 2017; 41:192. [PMID: 29075939 DOI: 10.1007/s10916-017-0834-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/04/2017] [Indexed: 11/29/2022]
Abstract
Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance. The proposed methodology for automated evaluation of parasites includes pre-processing of blood smear microscopic images followed by erythrocytes segmentation. To differentiate between different parasites; a total of 138 quantitative features characterising colour, morphology, and texture are extracted from segmented erythrocytes. An integrated pattern classification framework is designed where four feature selection methods viz. Correlation-based Feature Selection (CFS), Chi-square, Information Gain, and RELIEF are employed with three different classifiers i.e. Naive Bayes', C4.5, and Instance-Based Learning (IB1) individually. Optimal features subset with the best classifier is selected for achieving maximum diagnostic precision. It is seen that the proposed method achieved with 99.2% sensitivity and 99.6% specificity by combining CFS and C4.5 in comparison with other methods. Moreover, the web-based tool is entirely designed using open standards like Java for a web application, ImageJ for image processing, and WEKA for data mining considering its feasibility in rural places with minimal health care facilities.
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Affiliation(s)
- Maitreya Maity
- School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Dhiraj Dhane
- School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Tushar Mungle
- School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - A K Maiti
- Department of Pathology, Midnapur Medical College and Hospital, Medinipur, West Bengal, India
| | - Chandan Chakraborty
- School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
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Mata C, Oliver A, Lalande A, Walker P, Martí J. On the Use of XML in Medical Imaging Web-Based Applications. Ing Rech Biomed 2017. [DOI: 10.1016/j.irbm.2016.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Chankong T, Theera-Umpon N, Auephanwiriyakul S. Automatic cervical cell segmentation and classification in Pap smears. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 113:539-56. [PMID: 24433758 DOI: 10.1016/j.cmpb.2013.12.012] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Revised: 10/26/2013] [Accepted: 12/18/2013] [Indexed: 05/26/2023]
Abstract
Cervical cancer is one of the leading causes of cancer death in females worldwide. The disease can be cured if the patient is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is Papanicolaou test or Pap test. In this research, a method for automatic cervical cancer cell segmentation and classification is proposed. A single-cell image is segmented into nucleus, cytoplasm, and background, using the fuzzy C-means (FCM) clustering technique. Four cell classes in the ERUDIT and LCH datasets, i.e., normal, low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC), are considered. The 2-class problem can be achieved by grouping the last 3 classes as one abnormal class. Whereas, the Herlev dataset consists of 7 cell classes, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. These 7 classes can also be grouped to form a 2-class problem. These 3 datasets were tested on 5 classifiers including Bayesian classifier, linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural networks (ANN), and support vector machine (SVM). For the ERUDIT dataset, ANN with 5 nucleus-based features yielded the accuracies of 96.20% and 97.83% on the 4-class and 2-class problems, respectively. For the Herlev dataset, ANN with 9 cell-based features yielded the accuracies of 93.78% and 99.27% for the 7-class and 2-class problems, respectively. For the LCH dataset, ANN with 9 cell-based features yielded the accuracies of 95.00% and 97.00% for the 4-class and 2-class problems, respectively. The segmentation and classification performances of the proposed method were compared with that of the hard C-means clustering and watershed technique. The results show that the proposed automatic approach yields very good performance and is better than its counterparts.
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Affiliation(s)
- Thanatip Chankong
- Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
| | - Nipon Theera-Umpon
- Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand; Biomedical Engineering Center, Chiang Mai University, Chiang Mai 50200, Thailand.
| | - Sansanee Auephanwiriyakul
- Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand; Biomedical Engineering Center, Chiang Mai University, Chiang Mai 50200, Thailand.
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Mahmoudi SE, Akhondi-Asl A, Rahmani R, Faghih-Roohi S, Taimouri V, Sabouri A, Soltanian-Zadeh H. Web-based interactive 2D/3D medical image processing and visualization software. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2010; 98:172-182. [PMID: 20022133 DOI: 10.1016/j.cmpb.2009.11.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2009] [Revised: 11/16/2009] [Accepted: 11/19/2009] [Indexed: 05/28/2023]
Abstract
There are many medical image processing software tools available for research and diagnosis purposes. However, most of these tools are available only as local applications. This limits the accessibility of the software to a specific machine, and thus the data and processing power of that application are not available to other workstations. Further, there are operating system and processing power limitations which prevent such applications from running on every type of workstation. By developing web-based tools, it is possible for users to access the medical image processing functionalities wherever the internet is available. In this paper, we introduce a pure web-based, interactive, extendable, 2D and 3D medical image processing and visualization application that requires no client installation. Our software uses a four-layered design consisting of an algorithm layer, web-user-interface layer, server communication layer, and wrapper layer. To compete with extendibility of the current local medical image processing software, each layer is highly independent of other layers. A wide range of medical image preprocessing, registration, and segmentation methods are implemented using open source libraries. Desktop-like user interaction is provided by using AJAX technology in the web-user-interface. For the visualization functionality of the software, the VRML standard is used to provide 3D features over the web. Integration of these technologies has allowed implementation of our purely web-based software with high functionality without requiring powerful computational resources in the client side. The user-interface is designed such that the users can select appropriate parameters for practical research and clinical studies.
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Affiliation(s)
- Seyyed Ehsan Mahmoudi
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
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Costa C, Oliveira JL, Silva A, Ribeiro VG, Ribeiro J. Design, development, exploitation and assessment of a Cardiology Web PACS. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2009; 93:273-282. [PMID: 19117637 DOI: 10.1016/j.cmpb.2008.10.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2008] [Revised: 10/30/2008] [Accepted: 10/30/2008] [Indexed: 05/27/2023]
Abstract
Healthcare institutions are increasingly turning to digital medical imaging systems to promote better diagnosis and treatment of their patients. The implementation of the Picture Archiving and Communication System (PACS) clearly contributes to an increase in the productivity of health professionals. However, despite the amount of research that has been done in the past two decades, there are still several technological hurdles that hinder the wide adoption of PACS in the Web environment. In this paper, we present a Web-enabled PACS that through the inclusion of several DICOM services and compression methods promotes medical image availability and greater accessibility to users.
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Affiliation(s)
- Carlos Costa
- University of Aveiro-DETI/IEETA, Aveiro, Portugal.
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Yokohama N, Tsuchimoto T, Oishi M, Itou K. [Development and evaluation of the medical imaging distribution system with dynamic web application and clustering technology]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2007; 63:75-84. [PMID: 17344636 DOI: 10.6009/jjrt.63.75] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
It has been noted that the downtime of medical informatics systems is often long. Many systems encounter downtimes of hours or even days, which can have a critical effect on daily operations. Such systems remain especially weak in the areas of database and medical imaging data. The scheme design shows the three-layer architecture of the system: application, database, and storage layers. The application layer uses the DICOM protocol (Digital Imaging and Communication in Medicine) and HTTP (Hyper Text Transport Protocol) with AJAX (Asynchronous JavaScript+XML). The database is designed to decentralize in parallel using cluster technology. Consequently, restoration of the database can be done not only with ease but also with improved retrieval speed. In the storage layer, a network RAID (Redundant Array of Independent Disks) system, it is possible to construct exabyte-scale parallel file systems that exploit storage spread. Development and evaluation of the test-bed has been successful in medical information data backup and recovery in a network environment. This paper presents a schematic design of the new medical informatics system that can be accommodated from a recovery and the dynamic Web application for medical imaging distribution using AJAX.
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