Morphology-based interslice interpolation using local intensity information for segmentation

Liao, Xiaochun, Reutens, David and Yang, Zhengyi (2011). Morphology-based interslice interpolation using local intensity information for segmentation. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), Shanghai, China, (384-389). 15-17 October 2011. doi:10.1109/BMEI.2011.6098315

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Author Liao, Xiaochun
Reutens, David
Yang, Zhengyi
Title of paper Morphology-based interslice interpolation using local intensity information for segmentation
Conference name 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)
Conference location Shanghai, China
Conference dates 15-17 October 2011
Proceedings title 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)
Journal name Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Sub-type Fully published paper
DOI 10.1109/BMEI.2011.6098315
ISBN 9781424493517
Volume 1
Start page 384
End page 389
Total pages 6
Collection year 2012
Language eng
Abstract/Summary The interpolation of contours between slices in the absence of the original intensity image has been a challenging task and investigated for many years. In some applications of medical imaging, however, objects of interest are segmented manually on selected slices and the intensity image is available. The latter can be used to improve the quality of interpolated segmentations. In this paper, we present a two-step approach to accurate interslice interpolation of manual segmentations using information from both object shape and image intensity. Morphology based shape interpolation followed by the application of intensity-based neighborhood voting to adjust boundary voxels were used to integrate the two information sources. We compared our method to three existing interpolation methods for magnetic resonance images of mouse and human brain. The proposed method outperformed the three methods, having lower average error rates.
Keyword Interslice interpolation
Shape-based interpolation
Mathematical morphology
Conditional dilation
Local intensity information
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Collections: Official 2012 Collection
Centre for Advanced Imaging Publications
 
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Created: Thu, 19 Jan 2012, 12:05:39 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging