JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
3D Segmentation for High-Resolution Image Datasets Using a Commercial Editing Tool in the IoT Environment |
| 영문제목(English Title) |
3D Segmentation for High-Resolution Image Datasets Using a Commercial Editing Tool in the IoT Environment |
| 저자(Author) |
Koojoo Kwon
Byeong-Seok Shin
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| 원문수록처(Citation) |
VOL 13 NO. 05 PP. 1126 ~ 1134 (2017. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
A variety of medical service applications in the field of the Internet of Things (IoT) are being studied. Segmentation is important to identify meaningful regions in images and is also required in 3D images. Previous methods have been based on gray value and shape. The Visible Korean dataset consists of serially sectioned high-resolution color images. Unlike computed tomography or magnetic resonance images, automatic segmentation of color images is difficult because detecting an object’s boundaries in colored images is very difficult compared to grayscale images. Therefore, skilled anatomists usually segment color images manually or semi-automatically. We present an out-of-core 3D segmentation method for large-scale image datasets. Our method can segment significant regions in the coronal and sagittal planes, as well as the axial plane, to produce a 3D image. Our system verifies the result interactively with a multi-planar reconstruction view and a 3D view. Our system can be used to train unskilled anatomists and medical students. It is also possible for a skilled anatomist to segment an image remotely since it is difficult to transfer such large amounts of data. |
| 키워드(Keyword) |
Segmentation
Large-Scale Image
Photo Editing
Visible Korean
Volume Rendering
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| 파일첨부 |
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