3D Segmentation for High-Resolution Image Datasets Using a Commercial Editing Tool in the IoT Environment

Koojoo Kwon and Byeong-Seok Shin
Volume: 13, No: 5, Page: 1126 ~ 1134, Year: 2017
10.3745/JIPS.02.0070
Keywords: Segmentation, Large-Scale Image, Photo Editing, Visible Korean, Volume Rendering
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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.

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Cite this article
IEEE Style
Koojoo Kwon and Byeong-Seok Shin, "3D Segmentation for High-Resolution Image Datasets Using a Commercial Editing Tool in the IoT Environment," Journal of Information Processing Systems, vol. 13, no. 5, pp. 1126~1134, 2017. DOI: 10.3745/JIPS.02.0070.

ACM Style
Koojoo Kwon and Byeong-Seok Shin, "3D Segmentation for High-Resolution Image Datasets Using a Commercial Editing Tool in the IoT Environment," Journal of Information Processing Systems, 13, 5, (2017), 1126~1134. DOI: 10.3745/JIPS.02.0070.