A Semi-automated Approach to Improve the Efficiency of Medical Imaging Segmentation for Haptic Rendering.
The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography...
| Publicado en: | Journal of Digital Imaging Vol. 30; no. 4; pp. 519 - 528 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124395645&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124395645 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2017 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124395645 124395645 143940944 124395645 10.1007/s10278-017-9985-2 124395645 ppf: 519 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A Semi-automated Approach to Improve the Efficiency of Medical Imaging Segmentation for Haptic Rendering. aug: au: Banerjee, Pat Hu, Mengqi Kannan, Rahul Krishnaswamy, Srinivasan affil: Department of Mechanical and Industrial Engineering , MC 251, 2039 ERF, 842 W. Taylor St. Chicago 60607 USA sug: subj: Computer Simulation Imaging, Three-Dimensional Image Processing, Computer Assisted Tomography, X-Ray Computed DICOM Algorithms Data Analysis Software Magnetic Resonance Imaging ab: The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT). Image segmentation techniques are used to determine the anatomies of interest from the images. 3D models are obtained from segmentation and their triangle reduction is required for graphics and haptics rendering. This paper focuses on creating 3D models by automating the segmentation of CT images based on the pixel contrast for integrating the interface between Sensimmer and medical imaging devices, using the volumetric approach, Hough transform method, and manual centering method. Hence, automating the process has reduced the segmentation time by 56.35% while maintaining the same accuracy of the output at ±2 voxels. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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