Document Detail


Automated optic disk boundary detection by modified active contour model.
MedLine Citation:
PMID:  17355059     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) <3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM).
Authors:
Juan Xu; Opas Chutatape; Paul Chew
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Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  54     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2007 Mar 
Date Detail:
Created Date:  2007-03-14     Completed Date:  2007-12-17     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  473-82     Citation Subset:  IM    
Affiliation:
Department of Ophthalmology, School of Medicine, University of Pittsburgh, 203 Lothrop Street, EEI-834, Pittsburgh, PA 15213, USA. xxujuan@pmail.ntu.edu.sg
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Artificial Intelligence*
Color
Colorimetry / methods
Computer Simulation
Humans
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Models, Biological
Ophthalmoscopy / methods*
Optic Disk / anatomy & histology*
Pattern Recognition, Automated / methods*
Photography / methods*
Reproducibility of Results
Sensitivity and Specificity

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


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