Document Detail


A fully automatic method for lung parenchyma segmentation and repairing.
MedLine Citation:
PMID:  23053904     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Considering that the traditional lung segmentation algorithms are not adaptive for the situations that most of the juxtapleural nodules, which are excluded as fat, and lung are not segmented perfectly. In this paper, several methods are comprehensively utilized including optimal iterative threshold, three-dimensional connectivity labeling, three-dimensional region growing for the initial segmentation of the lung parenchyma, based on improved chain code, and Bresenham algorithms to repair the lung parenchyma. The paper thus proposes a fully automatic method for lung parenchyma segmentation and repairing. Ninety-seven lung nodule thoracic computed tomography scans and 25 juxtapleural nodule scans are used to test the proposed method and compare with the most-cited rolling-ball method. Experimental results show that the algorithm can segment lung parenchyma region automatically and accurately. The sensitivity of juxtapleural nodule inclusion is 100 %, the segmentation accuracy of juxtapleural nodule regions is 98.6 %, segmentation accuracy of lung parenchyma is more than 95.2 %, and the average segmentation time is 0.67 s/frame. The algorithm can achieve good results for lung parenchyma segmentation and repairing in various cases that nodules/tumors adhere to lung wall.
Authors:
Ying Wei; Guo Shen; Juan-juan Li
Related Documents :
24558804 - Determination of horizontal and vertical distribution of calabrian pine stands in turke...
14511514 - Rate models for conductance-based cortical neuronal networks.
23261164 - Segmentation of brain tissues using a 3-d multi-layer hidden markov model.
17945894 - Classifying ovarian tumors using bayesian multi-layer perceptrons and automatic relevan...
17820424 - The greenhouse effect: science and policy.
10909104 - Developments in census taking in the last 25 years.
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:  483-95     Citation Subset:  IM    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Algorithms*
Humans
Imaging, Three-Dimensional
Pattern Recognition, Automated / methods
Radiographic Image Interpretation, Computer-Assisted / methods*
Solitary Pulmonary Nodule / radiography*
Tomography, X-Ray Computed*
Comments/Corrections

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


Previous Document:  Preclinical Efficacy of Melatonin to Reduce Methotrexate-Induced Oxidative Stress and Small Intestin...
Next Document:  A knowledge-based approach for carpal tunnel segmentation from magnetic resonance images.