Document Detail


A Novel Approach for Lung Nodules Segmentation in Chest CT using Level Sets.
MedLine Citation:
PMID:  24107934     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
A new variational level set approach is proposed for lung nodule segmentation in lung CT scans. A general lung nodule shape model is proposed using implicit spaces as a signed distance function. The shape model is fused with the image intensity statistical information in a variational segmentation framework. The nodule shape model is mapped to the image domain by a global transformation that includes inhomogeneous scales, rotation, and translation parameters. A matching criteria between the shape model and the image implicit representations is employed in order to handle the alignment process. Transformation parameters evolve through gradient descent optimization to handle the shape alignment process and hence mark the boundaries of the nodule "head". The embedding process takes into consideration the image intensity as well as prior shape information. A non-parametric density estimation approach is employed to handle the statistical intensity representation of the nodule and background regions. The proposed technique does not depend on nodule type or location. Exhaustive experimental and validation results are demonstrated on 742 nodules obtained from four different CT lung databases, illustrating the robustness of the approach.
Authors:
Amal Farag; Hossam Abdelmunim; James Graham; Aly Farag
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-9-20
Journal Detail:
Title:  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society     Volume:  -     ISSN:  1941-0042     ISO Abbreviation:  IEEE Trans Image Process     Publication Date:  2013 Sep 
Date Detail:
Created Date:  2013-10-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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