Automated body-lung-air material map segmentation from pre-clinical MRI images for PET attenuation correction in Tera-Tomo 3D PET reconstruction engine of nanoScan PET/MRI system

Bándi, Péter; Jakab, Gábor; Zsótér, Norbert; Máthé, Domokos; Németh, Gábor; Nagy, Kálmán; Hobor, Sandor; Papp, László

doi:10.1186/2197-7364-1-s1-a86

Cited by 1 publication

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“…Diagnostic visualization is a useful research area in medicine that provides greatly increased knowledge of normal and diseased anatomy for medical research [1,2]. The increasing size and number of medical images requires the use of a computer to facilitate processing and analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Kwon¹,

Shin²

2017

J Inf Process Syst

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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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Section: Introductionmentioning

confidence: 99%