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:  Publisher    
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
Related Documents :
23495167 - Overcoming challenges associated with upright imaging of the cervicothoracic junction: ...
20801727 - Feeling body dissatisfied after viewing thin-ideal pictures is mediated by self-activat...
18089247 - Day-to-day body-image states: prospective predictors of intra-individual level and vari...
24602827 - Assessment of data acquisition parameters, and analysis techniques for noise reduction ...
18285987 - Simultaneous measurement of diameter and position of spherical particles in a spray by ...
23165057 - Total variation wavelet-based medical image denoising.
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-11-13
Journal Detail:
Title:  Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology     Volume:  -     ISSN:  1618-727X     ISO Abbreviation:  J Digit Imaging     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9100529     Medline TA:  J Digit Imaging     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

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


Previous Document:  West Nile virus ecology in a tropical ecosystem in Guatemala.
Next Document:  Stent salvage using the Enterprise stent for procedure-related complication during coil embolization...