Document Detail


Adaptive segmentation of vertebral bodies from sagittal MR images based on local spatial information and Gaussian weighted chi-square distance.
MedLine Citation:
PMID:  23149587     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We present a novel method for the automatic segmentation of the vertebral bodies from 2D sagittal magnetic resonance (MR) images of the spine. First, a new affinity matrix is constructed by incorporating neighboring information, which local intensity is considered to depict the image and overcome the noise effectively. Second, the Gaussian kernel function is to weight chi-square distance based on the neighboring information, which the vital spatial structure of the image is introduced to improve the accuracy of the segmentation task. Third, an adaptive local scaling parameter is utilized to facilitate the image segmentation and avoid the optimal configuration of controlling parameter manually. The encouraging results on the spinal MR images demonstrate the advantage of the proposed method over other methods in terms of both efficiency and robustness.
Authors:
Qian Zheng; Zhentai Lu; Qianjin Feng; Jianhua Ma; Wei Yang; Chao Chen; Wufan Chen
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Journal of digital imaging     Volume:  26     ISSN:  1618-727X     ISO Abbreviation:  J Digit Imaging     Publication Date:  2013 Jun 
Date Detail:
Created Date:  2013-05-09     Completed Date:  2014-01-09     Revised Date:  2014-06-03    
Medline Journal Info:
Nlm Unique ID:  9100529     Medline TA:  J Digit Imaging     Country:  United States    
Other Details:
Languages:  eng     Pagination:  578-93     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Chi-Square Distribution
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Sensitivity and Specificity
Spinal Diseases / diagnosis*
Spine / anatomy & histology*
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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