INTEGRATING SPATIAL FUZZY CLUSTERING
WITH LEVEL SET METHODS FOR LIVER
SEGMENTATION FROM COMPUTED
TOMOGRAPHY SCANS
AIRCC (Ред.) Computer Applications: An International Journal (CAIJ), 1 (1):
1-7(2014/08 2014)
Аннотация
This article presents a fully automatic segmentation method of liver CT scans using fuzzy cmean
clustering and level set. First, the difference of unique image is improved to make
boundaries clearer; second, a spatial fuzzy c-mean clustering combining with anatomical
previous information is engaged to extract liver area automatically Thirdly, a distance
regularized level set is used for modification; finally, morphological operations are used as postprocessing.
The experiment result shows that the method can achieve high accuracy (0.9986)
and specificity (0.9989). Comparing with standard level set method, our method is more
successful in dealing with over-segmentation difficulty.
%0 Journal Article
%1 aircc2014integrating
%D 2014
%E AIRCC,
%J Computer Applications: An International Journal (CAIJ)
%K tag
%N 1
%P 1-7
%T INTEGRATING SPATIAL FUZZY CLUSTERING
WITH LEVEL SET METHODS FOR LIVER
SEGMENTATION FROM COMPUTED
TOMOGRAPHY SCANS
%U http://airccse.com/caij/papers/1114caij05.pdf
%V 1
%X This article presents a fully automatic segmentation method of liver CT scans using fuzzy cmean
clustering and level set. First, the difference of unique image is improved to make
boundaries clearer; second, a spatial fuzzy c-mean clustering combining with anatomical
previous information is engaged to extract liver area automatically Thirdly, a distance
regularized level set is used for modification; finally, morphological operations are used as postprocessing.
The experiment result shows that the method can achieve high accuracy (0.9986)
and specificity (0.9989). Comparing with standard level set method, our method is more
successful in dealing with over-segmentation difficulty.
@article{aircc2014integrating,
abstract = { This article presents a fully automatic segmentation method of liver CT scans using fuzzy cmean
clustering and level set. First, the difference of unique image is improved to make
boundaries clearer; second, a spatial fuzzy c-mean clustering combining with anatomical
previous information is engaged to extract liver area automatically Thirdly, a distance
regularized level set is used for modification; finally, morphological operations are used as postprocessing.
The experiment result shows that the method can achieve high accuracy (0.9986)
and specificity (0.9989). Comparing with standard level set method, our method is more
successful in dealing with over-segmentation difficulty. },
added-at = {2018-01-29T07:07:59.000+0100},
biburl = {https://www.bibsonomy.org/bibtex/2b2db7a9ab965903019e80d4154ef49fd/caij},
editor = {AIRCC},
interhash = {70300d00746dd70102a5c2082d253525},
intrahash = {b2db7a9ab965903019e80d4154ef49fd},
journal = {Computer Applications: An International Journal (CAIJ)},
keywords = {tag},
month = {2014/08},
number = 1,
pages = {1-7},
timestamp = {2018-01-29T07:07:59.000+0100},
title = {INTEGRATING SPATIAL FUZZY CLUSTERING
WITH LEVEL SET METHODS FOR LIVER
SEGMENTATION FROM COMPUTED
TOMOGRAPHY SCANS },
url = {http://airccse.com/caij/papers/1114caij05.pdf},
volume = 1,
year = 2014
}