Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT
Internet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places. Medical image segmentation is needed for analysis, storage, and protection of medical image in telemedicine. Therefore, a var...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/815039 |
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| _version_ | 1832558557769236480 |
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| author | Seung-Hoon Chae Daesung Moon Deok Gyu Lee Sung Bum Pan |
| author_facet | Seung-Hoon Chae Daesung Moon Deok Gyu Lee Sung Bum Pan |
| author_sort | Seung-Hoon Chae |
| collection | DOAJ |
| description | Internet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places. Medical image segmentation is needed for analysis, storage, and protection of medical image in telemedicine. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Performing segmentation in various organs, the accurate judgment of the region is needed in medical image. However, the removal of region occurs by the lack of information to determine the region in a small region. In this paper, we researched how to reconstruct segmentation region in a small region in order to improve the segmentation results. We generated predicted segmentation of slices using volume data with linear equation and proposed improvement method for small regions using the predicted segmentation. In order to verify the performance of the proposed method, lung region by chest CT images was segmented. As a result of experiments, volume data segmentation accuracy rose from 0.978 to 0.981 and from 0.281 to 0.187 with a standard deviation improvement confirmed. |
| format | Article |
| id | doaj-art-e470bc03ee284fe689e64e3d93b109cd |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-e470bc03ee284fe689e64e3d93b109cd2025-02-03T01:32:06ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/815039815039Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoTSeung-Hoon Chae0Daesung Moon1Deok Gyu Lee2Sung Bum Pan3The Research Institute of IT, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, Republic of KoreaElectronics and Telecommunications Research Institute, 161 Gajeong-dong, Yuseong-gu, Daejeon 305-350, Republic of KoreaDepartment of Information Security, Seowon University, 377-3 Musimseo-ro, Heungdeok-gu, Cheongju-si, Choong-Chung Buk-do 361-742, Republic of KoreaDepartment of Electronics Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, Republic of KoreaInternet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places. Medical image segmentation is needed for analysis, storage, and protection of medical image in telemedicine. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Performing segmentation in various organs, the accurate judgment of the region is needed in medical image. However, the removal of region occurs by the lack of information to determine the region in a small region. In this paper, we researched how to reconstruct segmentation region in a small region in order to improve the segmentation results. We generated predicted segmentation of slices using volume data with linear equation and proposed improvement method for small regions using the predicted segmentation. In order to verify the performance of the proposed method, lung region by chest CT images was segmented. As a result of experiments, volume data segmentation accuracy rose from 0.978 to 0.981 and from 0.281 to 0.187 with a standard deviation improvement confirmed.http://dx.doi.org/10.1155/2014/815039 |
| spellingShingle | Seung-Hoon Chae Daesung Moon Deok Gyu Lee Sung Bum Pan Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT Journal of Applied Mathematics |
| title | Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT |
| title_full | Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT |
| title_fullStr | Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT |
| title_full_unstemmed | Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT |
| title_short | Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT |
| title_sort | medical image segmentation for mobile electronic patient charts using numerical modeling of iot |
| url | http://dx.doi.org/10.1155/2014/815039 |
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