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

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.

Auxiliary Application of CAD Technology Under the Internet of Things Platform in Painting Creation

“…developed a comprehensive method of generating three-dimensional color based on the parameterized object model of conformal mesh and the technology of direct texture mapping from object images [7]. Kwon and shin [8] proposed a method that can segment important areas in the coronal plane, sagittal plane, and axial plane to generate 3D images. Creighton et al [9] described a highly versatile in-situ strategy, which uses collectors with insulating surface layers and conductive recess patterns to pattern three-dimensional electrospun fibers.…”

2PN: A Unified Panoptic Segmentation Network with Attention Module

“…AI-empowered IOT: some research in different fields on the Internet of things focuses on datasets. [26] presents an out-of-core 3D segmentation method for large-scale image datasets on medical service. [26] introduces the novel concept of ϵ-Kernel Dataset on Wireless Sensor Networks (WSNs) and designs a distributed algorithm to satisfy the ϵ requirement.…”

“…[26] presents an out-of-core 3D segmentation method for large-scale image datasets on medical service. [26] introduces the novel concept of ϵ-Kernel Dataset on Wireless Sensor Networks (WSNs) and designs a distributed algorithm to satisfy the ϵ requirement. [27][28][29] also propose algorithms for WSNs, while our approach focuses on the Cityscapes dataset, which is the representative of the open environment of street scenes.…”

Efficient Approaches to Computer Vision and Pattern Recognition

“…Kwon and Shin [9] presented 3D segmentation for high-resolution image datasets for medical service applications. This paper focuses on the dataset of the Visible Korean project in Korea.…”